Working report
- Survey questions
- Q1. Before receiving this survey, did you know influenza is different from the stomach flu?
- Q2. Have you had an illness with influenza-like symptoms since August 2015?
- Q3. Has any other person in your household had an illness with influenza like symptoms since August 2015?
- Q4. Does your job require you to have a lot of contact with the public?
- Q5. Do you have a car that you can use to travel to work?
- Q6. Do you regularly use public transportation?
- Q7. What types of public transportation do you regularly use?
- Household Members
Survey questions
Q1. Before receiving this survey, did you know influenza is different from the stomach flu?
# Q1
with(data2, table(Q1))## Q1
## No Yes
## 488 1664q1 <- data2 %>%
count(Q1)
# plot
ggplot(data2[!is.na(data2$Q1), ]) + geom_bar(mapping = aes(x = Q1, fill = Q1))
# alternative plot:
#ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = Q1)) +
# geom_bar(stat = 'identity', position = position_dodge())
# by gender, PPGENDER
with(data2, table(PPGENDER, Q1))## Q1
## PPGENDER No Yes
## Female 205 888
## Male 283 776q1 <- data2 %>%
count(Q1, PPGENDER)
# plot
ggplot(data2[!is.na(data2$Q1), ]) + geom_bar(mapping = aes(x = Q1, fill = PPGENDER), position = position_dodge())
# plot with facet
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = Q1)) +
geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~PPGENDER)
# by ethnicity, PPETHM
with(data2, table(PPETHM, Q1))## Q1
## PPETHM No Yes
## White, Non-Hispanic 322 1235
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 29 63
## Hispanic 69 161
## 2+ races, Non-Hispanic 0 0q1 <- data2 %>%
count(Q1, PPETHM)
# plot
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = PPETHM)) +
geom_bar(stat = 'identity', position = position_dodge())
# plot with facet
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = Q1)) +
geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~PPETHM)
# by income, PPINCIMP
with(data2, table(PPINCIMP, Q1))## Q1
## PPINCIMP No Yes
## Less than $5,000 22 30
## $5,000 to $7,499 8 16
## $7,500 to $9,999 7 7
## $10,000 to $12,499 17 39
## $12,500 to $14,999 10 38
## $15,000 to $19,999 22 40
## $20,000 to $24,999 16 55
## $25,000 to $29,999 23 76
## $30,000 to $34,999 21 70
## $35,000 to $39,999 31 72
## $40,000 to $49,999 42 107
## $50,000 to $59,999 46 137
## $60,000 to $74,999 50 172
## $75,000 to $84,999 26 133
## $85,000 to $99,999 33 120
## $100,000 to $124,999 56 269
## $125,000 to $149,999 24 108
## $150,000 to $174,999 16 68
## $175,000 or more 18 107q1 <- data2 %>%
count(Q1, PPINCIMP)
# plot
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = PPINCIMP)) +
geom_bar(stat = 'identity', position = position_dodge())
# plot with facet
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = Q1)) +
geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~PPINCIMP)
Q2. Have you had an illness with influenza-like symptoms since August 2015?
with(data2, table(Q2))## Q2
## No Yes
## 1735 414q2 <- data2 %>%
count(Q2)
ggplot(q2, aes(x = Q2, y = n, fill = Q2)) + geom_bar(stat = 'identity')
# by gender
with(data2, table(Q2, PPGENDER))## PPGENDER
## Q2 Female Male
## No 858 877
## Yes 234 180q2 <- data2 %>%
count(Q2, PPGENDER)
ggplot(q2, aes(x = Q2, y = n, fill = PPGENDER)) +
geom_bar(stat = 'identity', position = position_dodge())
# by ethnicity
with(data2, table(Q2, PPETHM))## PPETHM
## Q2 White, Non-Hispanic Black or African American, Non-Hispanic
## No 1287 0
## Yes 269 0
## PPETHM
## Q2 Other, Non-Hispanic Hispanic 2+ races, Non-Hispanic
## No 71 164 0
## Yes 22 65 0q2 <- data2 %>%
count(Q2, PPETHM)
ggplot(q2, aes(x = Q2, y = n, fill = PPETHM)) +
geom_bar(stat = 'identity', position = position_dodge())
# by income
with(data2, table(PPINCIMP, Q2))## Q2
## PPINCIMP No Yes
## Less than $5,000 43 9
## $5,000 to $7,499 19 6
## $7,500 to $9,999 13 1
## $10,000 to $12,499 38 17
## $12,500 to $14,999 39 9
## $15,000 to $19,999 46 15
## $20,000 to $24,999 55 17
## $25,000 to $29,999 79 19
## $30,000 to $34,999 74 18
## $35,000 to $39,999 85 18
## $40,000 to $49,999 121 27
## $50,000 to $59,999 155 27
## $60,000 to $74,999 172 50
## $75,000 to $84,999 130 29
## $85,000 to $99,999 123 29
## $100,000 to $124,999 265 61
## $125,000 to $149,999 112 20
## $150,000 to $174,999 62 21
## $175,000 or more 104 21q2 <- data2 %>%
count(Q2, PPINCIMP)
ggplot(q2, aes(x = Q2, y = n, fill = PPINCIMP)) +
geom_bar(stat = 'identity', position = position_dodge())
Q3. Has any other person in your household had an illness with influenza like symptoms since August 2015?
# all
with(data2, table(Q3))## Q3
## Don_t know No Yes
## 161 1608 383q3 <- data2 %>%
count(Q3)
ggplot(q3, aes(x = Q3, y = n, fill = Q3)) + geom_bar(stat = 'identity')
# by gender
with(data2, table(Q3, PPGENDER))## PPGENDER
## Q3 Female Male
## Don_t know 72 89
## No 804 804
## Yes 217 166q3 <- data2 %>%
count(Q3, PPGENDER)
ggplot(q3, aes(x = Q3, y = n, fill = PPGENDER)) +
geom_bar(stat = 'identity', position = position_dodge())
# by ethnicity
with(data2, table(Q3, PPETHM))## PPETHM
## Q3 White, Non-Hispanic Black or African American, Non-Hispanic
## Don_t know 95 0
## No 1197 0
## Yes 265 0
## PPETHM
## Q3 Other, Non-Hispanic Hispanic 2+ races, Non-Hispanic
## Don_t know 11 30 0
## No 59 146 0
## Yes 23 53 0q3 <- data2 %>%
count(Q3, PPETHM)
ggplot(q3, aes(x = Q3, y = n, fill = PPETHM)) +
geom_bar(stat = 'identity', position = position_dodge())
# by income
with(data2, table(PPINCIMP, Q3))## Q3
## PPINCIMP Don_t know No Yes
## Less than $5,000 11 36 5
## $5,000 to $7,499 6 18 1
## $7,500 to $9,999 1 13 0
## $10,000 to $12,499 4 44 8
## $12,500 to $14,999 7 30 11
## $15,000 to $19,999 7 47 8
## $20,000 to $24,999 8 52 12
## $25,000 to $29,999 4 81 13
## $30,000 to $34,999 11 70 9
## $35,000 to $39,999 11 75 17
## $40,000 to $49,999 6 117 25
## $50,000 to $59,999 13 136 33
## $60,000 to $74,999 18 165 39
## $75,000 to $84,999 7 120 33
## $85,000 to $99,999 11 107 35
## $100,000 to $124,999 20 245 61
## $125,000 to $149,999 6 100 26
## $150,000 to $174,999 3 58 23
## $175,000 or more 7 94 24q3 <- data2 %>%
count(Q3, PPINCIMP)
ggplot(q3, aes(x = Q3, y = n, fill = PPINCIMP)) +
geom_bar(stat = 'identity', position = position_dodge())
Q4. Does your job require you to have a lot of contact with the public?
# all
with(data2, table(Q4))## Q4
## No, I don_t work
## 779
## No, my job does not require much contact with the public
## 620
## Yes
## 751q4 <- data2 %>%
count(Q4)
ggplot(q4, aes(x = Q4, y = n, fill = Q4)) + geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# by gender
with(data2, table(Q4, PPGENDER))## PPGENDER
## Q4 Female Male
## No, I don_t work 430 349
## No, my job does not require much contact with the public 263 357
## Yes 400 351q4 <- data2 %>%
count(Q4, PPGENDER)
ggplot(q4, aes(x = Q4, y = n, fill = PPGENDER)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# by ethnicity
with(data2, table(PPETHM, Q4))## Q4
## PPETHM No, I don_t work
## White, Non-Hispanic 587
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 24
## Hispanic 69
## 2+ races, Non-Hispanic 0
## Q4
## PPETHM No, my job does not require much contact with the public
## White, Non-Hispanic 432
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 34
## Hispanic 72
## 2+ races, Non-Hispanic 0
## Q4
## PPETHM Yes
## White, Non-Hispanic 538
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 35
## Hispanic 87
## 2+ races, Non-Hispanic 0q4 <- data2 %>%
count(Q4, PPETHM)
ggplot(q4, aes(x = Q4, y = n, fill = PPETHM)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# by income
with(data2, table(PPINCIMP, Q4))## Q4
## PPINCIMP No, I don_t work
## Less than $5,000 29
## $5,000 to $7,499 15
## $7,500 to $9,999 11
## $10,000 to $12,499 33
## $12,500 to $14,999 32
## $15,000 to $19,999 28
## $20,000 to $24,999 35
## $25,000 to $29,999 46
## $30,000 to $34,999 38
## $35,000 to $39,999 42
## $40,000 to $49,999 64
## $50,000 to $59,999 60
## $60,000 to $74,999 73
## $75,000 to $84,999 45
## $85,000 to $99,999 47
## $100,000 to $124,999 87
## $125,000 to $149,999 39
## $150,000 to $174,999 23
## $175,000 or more 32
## Q4
## PPINCIMP No, my job does not require much contact with the public
## Less than $5,000 17
## $5,000 to $7,499 5
## $7,500 to $9,999 1
## $10,000 to $12,499 7
## $12,500 to $14,999 5
## $15,000 to $19,999 13
## $20,000 to $24,999 18
## $25,000 to $29,999 15
## $30,000 to $34,999 25
## $35,000 to $39,999 22
## $40,000 to $49,999 41
## $50,000 to $59,999 58
## $60,000 to $74,999 60
## $75,000 to $84,999 51
## $85,000 to $99,999 48
## $100,000 to $124,999 111
## $125,000 to $149,999 51
## $150,000 to $174,999 25
## $175,000 or more 47
## Q4
## PPINCIMP Yes
## Less than $5,000 6
## $5,000 to $7,499 5
## $7,500 to $9,999 2
## $10,000 to $12,499 15
## $12,500 to $14,999 11
## $15,000 to $19,999 21
## $20,000 to $24,999 19
## $25,000 to $29,999 37
## $30,000 to $34,999 29
## $35,000 to $39,999 39
## $40,000 to $49,999 43
## $50,000 to $59,999 63
## $60,000 to $74,999 88
## $75,000 to $84,999 64
## $85,000 to $99,999 58
## $100,000 to $124,999 127
## $125,000 to $149,999 42
## $150,000 to $174,999 36
## $175,000 or more 46q4 <- data2 %>%
count(Q4, PPINCIMP)
ggplot(q4, aes(x = Q4, y = n, fill = PPINCIMP)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Q5. Do you have a car that you can use to travel to work?
# all
with(data2, table(Q5))## Q5
## No Yes
## 133 1235q5 <- data2 %>%
count(Q5)
ggplot(q5, aes(x = Q5, y = n, fill = Q5)) + geom_bar(stat = 'identity')
# by gender
with(data2, table(PPGENDER, Q5))## Q5
## PPGENDER No Yes
## Female 70 592
## Male 63 643q5 <- data2 %>%
count(Q5, PPGENDER)
ggplot(q5, aes(x = Q5, y = n, fill = PPGENDER)) +
geom_bar(stat = 'identity', position = position_dodge())
# by ethnicity
with(data2, table(PPETHM, Q5))## Q5
## PPETHM No Yes
## White, Non-Hispanic 72 895
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 8 61
## Hispanic 24 135
## 2+ races, Non-Hispanic 0 0q5 <- data2 %>%
count(Q5, PPETHM)
ggplot(q5, aes(x = Q5, y = n, fill = PPETHM)) +
geom_bar(stat = 'identity', position = position_dodge())
# by income
with(data2, table(PPINCIMP, Q5))## Q5
## PPINCIMP No Yes
## Less than $5,000 6 17
## $5,000 to $7,499 5 5
## $7,500 to $9,999 1 2
## $10,000 to $12,499 3 19
## $12,500 to $14,999 3 13
## $15,000 to $19,999 4 30
## $20,000 to $24,999 6 30
## $25,000 to $29,999 8 44
## $30,000 to $34,999 7 47
## $35,000 to $39,999 9 52
## $40,000 to $49,999 11 72
## $50,000 to $59,999 12 109
## $60,000 to $74,999 9 138
## $75,000 to $84,999 13 102
## $85,000 to $99,999 8 98
## $100,000 to $124,999 8 230
## $125,000 to $149,999 5 88
## $150,000 to $174,999 7 54
## $175,000 or more 8 85q5 <- data2 %>%
count(Q5, PPINCIMP)
ggplot(q5, aes(x = Q5, y = n, fill = PPINCIMP)) +
geom_bar(stat = 'identity', position = position_dodge())
Q6. Do you regularly use public transportation?
with(data2, table(Q6))## Q6
## No Yes
## 1959 194q6 <- data2 %>%
count(Q6)
ggplot(q6, aes(x = Q6, y = n, fill = Q6)) + geom_bar(stat = 'identity')
# by gender
with(data2, table(PPGENDER, Q6))## Q6
## PPGENDER No Yes
## Female 998 96
## Male 961 98q6 <- data2 %>%
count(Q6, PPGENDER)
ggplot(q6, aes(x = Q6, y = n, fill = PPGENDER)) +
geom_bar(stat = 'identity', position = position_dodge())
# by ethnicity
with(data2, table(PPETHM, Q6))## Q6
## PPETHM No Yes
## White, Non-Hispanic 1463 95
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 80 13
## Hispanic 196 32
## 2+ races, Non-Hispanic 0 0q6 <- data2 %>%
count(Q6, PPETHM)
ggplot(q6, aes(x = Q6, y = n, fill = PPETHM)) +
geom_bar(stat = 'identity', position = position_dodge())
# by income
with(data2, table(PPINCIMP, Q6))## Q6
## PPINCIMP No Yes
## Less than $5,000 42 10
## $5,000 to $7,499 22 3
## $7,500 to $9,999 10 4
## $10,000 to $12,499 47 9
## $12,500 to $14,999 42 5
## $15,000 to $19,999 58 4
## $20,000 to $24,999 64 8
## $25,000 to $29,999 90 8
## $30,000 to $34,999 85 7
## $35,000 to $39,999 92 12
## $40,000 to $49,999 141 7
## $50,000 to $59,999 166 17
## $60,000 to $74,999 200 20
## $75,000 to $84,999 148 12
## $85,000 to $99,999 142 11
## $100,000 to $124,999 305 21
## $125,000 to $149,999 123 9
## $150,000 to $174,999 74 10
## $175,000 or more 108 17q6 <- data2 %>%
count(Q6, PPINCIMP)
ggplot(q6, aes(x = Q6, y = n, fill = PPINCIMP)) +
geom_bar(stat = 'identity', position = position_dodge())
Q7. What types of public transportation do you regularly use?
# look at patterned names
# grep("Q7", names(data2))
# make long data
q7_long <- data2 %>%
gather("Q7_q", "Q7_r", starts_with("Q7_"), -contains("Text"), -contains("Refused"), na.rm = TRUE)
#grep("Q7", names(q7_long))
#View(q7_long[c(1, 34, 35, 423:424)])
with(q7_long, table(Q7_q, Q7_r))## Q7_r
## Q7_q No Yes
## Q7_1_Bus 57 137
## Q7_2_Carpool 184 10
## Q7_3_Subway 131 63
## Q7_4_Train 139 55
## Q7_5_Taxi 169 25
## Q7_6_Airplane 175 19
## Q7_7_Other 179 15q7 <- q7_long %>%
count(Q7_q, Q7_r)
# flip coordinates
ggplot(q7[!is.na(q7$Q7_r), ], aes(x = Q7_r, y = n, fill = Q7_r)) +
geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~Q7_q) + coord_flip()
# by gender
with(q7_long, table(PPGENDER, Q7_q, Q7_r))## , , Q7_r = No
##
## Q7_q
## PPGENDER Q7_1_Bus Q7_2_Carpool Q7_3_Subway Q7_4_Train Q7_5_Taxi
## Female 27 91 68 75 81
## Male 30 93 63 64 88
## Q7_q
## PPGENDER Q7_6_Airplane Q7_7_Other
## Female 89 87
## Male 86 92
##
## , , Q7_r = Yes
##
## Q7_q
## PPGENDER Q7_1_Bus Q7_2_Carpool Q7_3_Subway Q7_4_Train Q7_5_Taxi
## Female 69 5 28 21 15
## Male 68 5 35 34 10
## Q7_q
## PPGENDER Q7_6_Airplane Q7_7_Other
## Female 7 9
## Male 12 6q7 <- q7_long %>%
group_by(PPGENDER, Q7_q, Q7_r) %>%
count(PPGENDER, Q7_q, Q7_r)
ggplot(q7[!is.na(q7$Q7_r), ], aes(x = Q7_r, y = n, fill = PPGENDER)) +
geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~Q7_q)
# by ethnicity
with(q7_long, table(PPETHM, Q7_q, Q7_r))## , , Q7_r = No
##
## Q7_q
## PPETHM Q7_1_Bus Q7_2_Carpool
## White, Non-Hispanic 33 91
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 6 13
## Hispanic 9 27
## 2+ races, Non-Hispanic 0 0
## Q7_q
## PPETHM Q7_3_Subway Q7_4_Train Q7_5_Taxi
## White, Non-Hispanic 65 67 84
## Black or African American, Non-Hispanic 0 0 0
## Other, Non-Hispanic 7 7 12
## Hispanic 21 23 27
## 2+ races, Non-Hispanic 0 0 0
## Q7_q
## PPETHM Q7_6_Airplane Q7_7_Other
## White, Non-Hispanic 81 85
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 12 13
## Hispanic 31 32
## 2+ races, Non-Hispanic 0 0
##
## , , Q7_r = Yes
##
## Q7_q
## PPETHM Q7_1_Bus Q7_2_Carpool
## White, Non-Hispanic 62 4
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 7 0
## Hispanic 23 5
## 2+ races, Non-Hispanic 0 0
## Q7_q
## PPETHM Q7_3_Subway Q7_4_Train Q7_5_Taxi
## White, Non-Hispanic 30 28 11
## Black or African American, Non-Hispanic 0 0 0
## Other, Non-Hispanic 6 6 1
## Hispanic 11 9 5
## 2+ races, Non-Hispanic 0 0 0
## Q7_q
## PPETHM Q7_6_Airplane Q7_7_Other
## White, Non-Hispanic 14 10
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 1 0
## Hispanic 1 0
## 2+ races, Non-Hispanic 0 0q7 <- q7_long %>%
group_by(PPETHM, Q7_q, Q7_r) %>%
count(PPETHM, Q7_q, Q7_r)
ggplot(q7[!is.na(q7$Q7_r), ], aes(x = Q7_r, y = n, fill = PPETHM)) +
geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~Q7_q)
# by income
with(q7_long, table(PPINCIMP, Q7_q, Q7_r ))## , , Q7_r = No
##
## Q7_q
## PPINCIMP Q7_1_Bus Q7_2_Carpool Q7_3_Subway Q7_4_Train
## Less than $5,000 0 10 9 8
## $5,000 to $7,499 3 2 3 2
## $7,500 to $9,999 2 4 3 2
## $10,000 to $12,499 3 9 9 8
## $12,500 to $14,999 0 5 5 4
## $15,000 to $19,999 1 4 4 4
## $20,000 to $24,999 2 7 7 6
## $25,000 to $29,999 0 7 7 7
## $30,000 to $34,999 1 6 6 6
## $35,000 to $39,999 2 12 7 9
## $40,000 to $49,999 4 6 5 5
## $50,000 to $59,999 6 17 12 12
## $60,000 to $74,999 2 19 15 17
## $75,000 to $84,999 4 11 5 8
## $85,000 to $99,999 3 9 6 7
## $100,000 to $124,999 8 21 11 11
## $125,000 to $149,999 3 9 6 6
## $150,000 to $174,999 4 9 4 7
## $175,000 or more 9 17 7 10
## Q7_q
## PPINCIMP Q7_5_Taxi Q7_6_Airplane Q7_7_Other
## Less than $5,000 9 9 9
## $5,000 to $7,499 3 3 2
## $7,500 to $9,999 3 4 3
## $10,000 to $12,499 7 9 7
## $12,500 to $14,999 5 5 5
## $15,000 to $19,999 3 4 4
## $20,000 to $24,999 8 8 7
## $25,000 to $29,999 6 8 8
## $30,000 to $34,999 4 6 7
## $35,000 to $39,999 11 10 11
## $40,000 to $49,999 7 7 7
## $50,000 to $59,999 13 15 16
## $60,000 to $74,999 19 20 16
## $75,000 to $84,999 9 10 12
## $85,000 to $99,999 10 8 10
## $100,000 to $124,999 20 19 20
## $125,000 to $149,999 9 9 9
## $150,000 to $174,999 9 7 9
## $175,000 or more 14 14 17
##
## , , Q7_r = Yes
##
## Q7_q
## PPINCIMP Q7_1_Bus Q7_2_Carpool Q7_3_Subway Q7_4_Train
## Less than $5,000 10 0 1 2
## $5,000 to $7,499 0 1 0 1
## $7,500 to $9,999 2 0 1 2
## $10,000 to $12,499 6 0 0 1
## $12,500 to $14,999 5 0 0 1
## $15,000 to $19,999 3 0 0 0
## $20,000 to $24,999 6 1 1 2
## $25,000 to $29,999 8 1 1 1
## $30,000 to $34,999 6 1 1 1
## $35,000 to $39,999 10 0 5 3
## $40,000 to $49,999 3 1 2 2
## $50,000 to $59,999 11 0 5 5
## $60,000 to $74,999 18 1 5 3
## $75,000 to $84,999 8 1 7 4
## $85,000 to $99,999 8 2 5 4
## $100,000 to $124,999 13 0 10 10
## $125,000 to $149,999 6 0 3 3
## $150,000 to $174,999 6 1 6 3
## $175,000 or more 8 0 10 7
## Q7_q
## PPINCIMP Q7_5_Taxi Q7_6_Airplane Q7_7_Other
## Less than $5,000 1 1 1
## $5,000 to $7,499 0 0 1
## $7,500 to $9,999 1 0 1
## $10,000 to $12,499 2 0 2
## $12,500 to $14,999 0 0 0
## $15,000 to $19,999 1 0 0
## $20,000 to $24,999 0 0 1
## $25,000 to $29,999 2 0 0
## $30,000 to $34,999 3 1 0
## $35,000 to $39,999 1 2 1
## $40,000 to $49,999 0 0 0
## $50,000 to $59,999 4 2 1
## $60,000 to $74,999 1 0 4
## $75,000 to $84,999 3 2 0
## $85,000 to $99,999 1 3 1
## $100,000 to $124,999 1 2 1
## $125,000 to $149,999 0 0 0
## $150,000 to $174,999 1 3 1
## $175,000 or more 3 3 0q7 <- q7_long %>%
group_by(PPINCIMP, Q7_q, Q7_r) %>%
count(PPINCIMP, Q7_q, Q7_r)
ggplot(q7[!is.na(q7$Q7_r), ], aes(x = Q7_r, y = n, fill = PPINCIMP)) +
geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~Q7_q)
Q8. For what types of activities do you regularly use public transportation?
q8_long <- data2 %>%
gather("Q8_q", "Q8_r", starts_with("Q8_"), -contains("otherText"), -contains("Refused"))
with(q8_long, table(Q8_q, Q8_r))## Q8_r
## Q8_q No Yes
## Q8_1_Work 89 105
## Q8_2_School 158 36
## Q8_3_Shopping 107 87
## Q8_4_Visiting.people 125 69
## Q8_5_Recreation 127 67
## Q8_6_Other 175 19q8 <- q8_long %>%
count(Q8_q, Q8_r)
# PPGENDER
with(q8_long, table(PPGENDER, Q8_q, Q8_r))## , , Q8_r = No
##
## Q8_q
## PPGENDER Q8_1_Work Q8_2_School Q8_3_Shopping Q8_4_Visiting.people
## Female 53 78 43 60
## Male 36 80 64 65
## Q8_q
## PPGENDER Q8_5_Recreation Q8_6_Other
## Female 64 84
## Male 63 91
##
## , , Q8_r = Yes
##
## Q8_q
## PPGENDER Q8_1_Work Q8_2_School Q8_3_Shopping Q8_4_Visiting.people
## Female 43 18 53 36
## Male 62 18 34 33
## Q8_q
## PPGENDER Q8_5_Recreation Q8_6_Other
## Female 32 12
## Male 35 7q8 <- q8_long %>%
group_by(PPGENDER, Q8_q, Q8_r) %>%
count(PPGENDER, Q8_q, Q8_r)
# PPETHM
with(q8_long, table(PPETHM, Q8_q, Q8_r))## , , Q8_r = No
##
## Q8_q
## PPETHM Q8_1_Work Q8_2_School
## White, Non-Hispanic 47 81
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 4 7
## Hispanic 14 25
## 2+ races, Non-Hispanic 0 0
## Q8_q
## PPETHM Q8_3_Shopping
## White, Non-Hispanic 55
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 9
## Hispanic 15
## 2+ races, Non-Hispanic 0
## Q8_q
## PPETHM Q8_4_Visiting.people
## White, Non-Hispanic 64
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 11
## Hispanic 20
## 2+ races, Non-Hispanic 0
## Q8_q
## PPETHM Q8_5_Recreation Q8_6_Other
## White, Non-Hispanic 58 84
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 11 13
## Hispanic 24 30
## 2+ races, Non-Hispanic 0 0
##
## , , Q8_r = Yes
##
## Q8_q
## PPETHM Q8_1_Work Q8_2_School
## White, Non-Hispanic 48 14
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 9 6
## Hispanic 18 7
## 2+ races, Non-Hispanic 0 0
## Q8_q
## PPETHM Q8_3_Shopping
## White, Non-Hispanic 40
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 4
## Hispanic 17
## 2+ races, Non-Hispanic 0
## Q8_q
## PPETHM Q8_4_Visiting.people
## White, Non-Hispanic 31
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 2
## Hispanic 12
## 2+ races, Non-Hispanic 0
## Q8_q
## PPETHM Q8_5_Recreation Q8_6_Other
## White, Non-Hispanic 37 11
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 2 0
## Hispanic 8 2
## 2+ races, Non-Hispanic 0 0# PPINCIMP
with(q8_long, table(PPINCIMP, Q8_q, Q8_r))## , , Q8_r = No
##
## Q8_q
## PPINCIMP Q8_1_Work Q8_2_School Q8_3_Shopping
## Less than $5,000 8 8 3
## $5,000 to $7,499 1 2 2
## $7,500 to $9,999 2 4 1
## $10,000 to $12,499 7 8 7
## $12,500 to $14,999 3 4 2
## $15,000 to $19,999 3 2 0
## $20,000 to $24,999 4 7 5
## $25,000 to $29,999 5 8 0
## $30,000 to $34,999 3 7 4
## $35,000 to $39,999 2 10 6
## $40,000 to $49,999 4 4 3
## $50,000 to $59,999 9 12 9
## $60,000 to $74,999 10 15 9
## $75,000 to $84,999 3 9 5
## $85,000 to $99,999 3 8 9
## $100,000 to $124,999 9 17 16
## $125,000 to $149,999 4 8 5
## $150,000 to $174,999 3 9 8
## $175,000 or more 6 16 13
## Q8_q
## PPINCIMP Q8_4_Visiting.people Q8_5_Recreation Q8_6_Other
## Less than $5,000 5 8 9
## $5,000 to $7,499 2 3 2
## $7,500 to $9,999 2 3 4
## $10,000 to $12,499 6 7 3
## $12,500 to $14,999 3 4 4
## $15,000 to $19,999 3 2 3
## $20,000 to $24,999 6 6 6
## $25,000 to $29,999 6 4 8
## $30,000 to $34,999 3 4 7
## $35,000 to $39,999 7 7 12
## $40,000 to $49,999 6 5 7
## $50,000 to $59,999 12 13 16
## $60,000 to $74,999 9 14 20
## $75,000 to $84,999 4 3 12
## $85,000 to $99,999 9 10 10
## $100,000 to $124,999 19 17 19
## $125,000 to $149,999 5 4 7
## $150,000 to $174,999 7 4 10
## $175,000 or more 11 9 16
##
## , , Q8_r = Yes
##
## Q8_q
## PPINCIMP Q8_1_Work Q8_2_School Q8_3_Shopping
## Less than $5,000 2 2 7
## $5,000 to $7,499 2 1 1
## $7,500 to $9,999 2 0 3
## $10,000 to $12,499 2 1 2
## $12,500 to $14,999 2 1 3
## $15,000 to $19,999 1 2 4
## $20,000 to $24,999 4 1 3
## $25,000 to $29,999 3 0 8
## $30,000 to $34,999 4 0 3
## $35,000 to $39,999 10 2 6
## $40,000 to $49,999 3 3 4
## $50,000 to $59,999 8 5 8
## $60,000 to $74,999 10 5 11
## $75,000 to $84,999 9 3 7
## $85,000 to $99,999 8 3 2
## $100,000 to $124,999 12 4 5
## $125,000 to $149,999 5 1 4
## $150,000 to $174,999 7 1 2
## $175,000 or more 11 1 4
## Q8_q
## PPINCIMP Q8_4_Visiting.people Q8_5_Recreation Q8_6_Other
## Less than $5,000 5 2 1
## $5,000 to $7,499 1 0 1
## $7,500 to $9,999 2 1 0
## $10,000 to $12,499 3 2 6
## $12,500 to $14,999 2 1 1
## $15,000 to $19,999 1 2 1
## $20,000 to $24,999 2 2 2
## $25,000 to $29,999 2 4 0
## $30,000 to $34,999 4 3 0
## $35,000 to $39,999 5 5 0
## $40,000 to $49,999 1 2 0
## $50,000 to $59,999 5 4 1
## $60,000 to $74,999 11 6 0
## $75,000 to $84,999 8 9 0
## $85,000 to $99,999 2 1 1
## $100,000 to $124,999 2 4 2
## $125,000 to $149,999 4 5 2
## $150,000 to $174,999 3 6 0
## $175,000 or more 6 8 1Q9. Do other members of your household regularly use public transportation?
with(data2, table(Q9))## Q9
## Don_t know No Yes
## 32 1935 183Q10. What types of public transportation do other members of your household regularly use?
#Q10 <- data2 %>%
# select(CaseID, PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, #Q10_1_Bus:Q10_9_Refused) %>%
# gather("Q10_q", "Q10_r", Q10_1_Bus:Q10_8_Other)
q10_long <- data2 %>%
gather("Q10_q", "Q10_r", starts_with("Q10_"), -contains("Text"), -contains("Refused"), na.rm = TRUE)
with(q10_long, table(Q10_q, Q10_r))## Q10_r
## Q10_q No Yes
## Q10_1_Bus 48 135
## Q10_2_Carpool 166 17
## Q10_3_Subway 130 53
## Q10_4_Train 137 46
## Q10_5_Taxi 157 26
## Q10_6_Airplane 164 19
## Q10_7_Don_t.know 182 1
## Q10_8_Other 172 11q10 <- q10_long %>%
count(Q10_q, Q10_r)
# PPGENDER
with(q10_long, table(PPGENDER, Q10_q, Q10_r))## , , Q10_r = No
##
## Q10_q
## PPGENDER Q10_1_Bus Q10_2_Carpool Q10_3_Subway Q10_4_Train Q10_5_Taxi
## Female 26 91 70 74 83
## Male 22 75 60 63 74
## Q10_q
## PPGENDER Q10_6_Airplane Q10_7_Don_t.know Q10_8_Other
## Female 91 99 92
## Male 73 83 80
##
## , , Q10_r = Yes
##
## Q10_q
## PPGENDER Q10_1_Bus Q10_2_Carpool Q10_3_Subway Q10_4_Train Q10_5_Taxi
## Female 73 8 29 25 16
## Male 62 9 24 21 10
## Q10_q
## PPGENDER Q10_6_Airplane Q10_7_Don_t.know Q10_8_Other
## Female 8 0 7
## Male 11 1 4q10 <- q10_long %>%
group_by(PPGENDER, Q10_q, Q10_r) %>%
count(PPGENDER, Q10_q, Q10_r)
# PPETHM
with(q10_long, table(PPETHM, Q10_q, Q10_r))## , , Q10_r = No
##
## Q10_q
## PPETHM Q10_1_Bus Q10_2_Carpool
## White, Non-Hispanic 23 78
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 5 11
## Hispanic 12 38
## 2+ races, Non-Hispanic 0 0
## Q10_q
## PPETHM Q10_3_Subway Q10_4_Train
## White, Non-Hispanic 65 61
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 10 10
## Hispanic 28 33
## 2+ races, Non-Hispanic 0 0
## Q10_q
## PPETHM Q10_5_Taxi Q10_6_Airplane
## White, Non-Hispanic 73 74
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 11 11
## Hispanic 37 37
## 2+ races, Non-Hispanic 0 0
## Q10_q
## PPETHM Q10_7_Don_t.know Q10_8_Other
## White, Non-Hispanic 84 77
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 14 14
## Hispanic 41 40
## 2+ races, Non-Hispanic 0 0
##
## , , Q10_r = Yes
##
## Q10_q
## PPETHM Q10_1_Bus Q10_2_Carpool
## White, Non-Hispanic 61 6
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 9 3
## Hispanic 29 3
## 2+ races, Non-Hispanic 0 0
## Q10_q
## PPETHM Q10_3_Subway Q10_4_Train
## White, Non-Hispanic 19 23
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 4 4
## Hispanic 13 8
## 2+ races, Non-Hispanic 0 0
## Q10_q
## PPETHM Q10_5_Taxi Q10_6_Airplane
## White, Non-Hispanic 11 10
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 3 3
## Hispanic 4 4
## 2+ races, Non-Hispanic 0 0
## Q10_q
## PPETHM Q10_7_Don_t.know Q10_8_Other
## White, Non-Hispanic 0 7
## Black or African American, Non-Hispanic 0 0
## Other, Non-Hispanic 0 0
## Hispanic 0 1
## 2+ races, Non-Hispanic 0 0# PPINCIMP
with(q10_long, table(PPINCIMP, Q10_q, Q10_r))## , , Q10_r = No
##
## Q10_q
## PPINCIMP Q10_1_Bus Q10_2_Carpool Q10_3_Subway Q10_4_Train
## Less than $5,000 0 8 8 7
## $5,000 to $7,499 2 2 1 1
## $7,500 to $9,999 1 2 1 0
## $10,000 to $12,499 2 5 5 4
## $12,500 to $14,999 0 6 5 6
## $15,000 to $19,999 0 2 2 2
## $20,000 to $24,999 2 8 12 11
## $25,000 to $29,999 0 6 4 5
## $30,000 to $34,999 2 5 4 5
## $35,000 to $39,999 2 8 5 4
## $40,000 to $49,999 3 7 6 5
## $50,000 to $59,999 2 14 9 9
## $60,000 to $74,999 3 17 16 19
## $75,000 to $84,999 2 12 10 10
## $85,000 to $99,999 3 8 2 5
## $100,000 to $124,999 9 23 16 15
## $125,000 to $149,999 4 11 6 9
## $150,000 to $174,999 4 12 9 10
## $175,000 or more 7 10 9 10
## Q10_q
## PPINCIMP Q10_5_Taxi Q10_6_Airplane Q10_7_Don_t.know
## Less than $5,000 7 8 8
## $5,000 to $7,499 2 2 2
## $7,500 to $9,999 1 2 2
## $10,000 to $12,499 4 4 5
## $12,500 to $14,999 6 6 6
## $15,000 to $19,999 2 2 2
## $20,000 to $24,999 9 11 12
## $25,000 to $29,999 6 6 6
## $30,000 to $34,999 5 5 5
## $35,000 to $39,999 8 8 9
## $40,000 to $49,999 7 7 7
## $50,000 to $59,999 11 13 15
## $60,000 to $74,999 22 22 22
## $75,000 to $84,999 13 13 12
## $85,000 to $99,999 6 7 8
## $100,000 to $124,999 20 21 23
## $125,000 to $149,999 10 10 11
## $150,000 to $174,999 8 9 14
## $175,000 or more 10 8 13
## Q10_q
## PPINCIMP Q10_8_Other
## Less than $5,000 8
## $5,000 to $7,499 1
## $7,500 to $9,999 2
## $10,000 to $12,499 5
## $12,500 to $14,999 6
## $15,000 to $19,999 2
## $20,000 to $24,999 10
## $25,000 to $29,999 5
## $30,000 to $34,999 4
## $35,000 to $39,999 9
## $40,000 to $49,999 7
## $50,000 to $59,999 15
## $60,000 to $74,999 21
## $75,000 to $84,999 13
## $85,000 to $99,999 8
## $100,000 to $124,999 21
## $125,000 to $149,999 11
## $150,000 to $174,999 12
## $175,000 or more 12
##
## , , Q10_r = Yes
##
## Q10_q
## PPINCIMP Q10_1_Bus Q10_2_Carpool Q10_3_Subway Q10_4_Train
## Less than $5,000 8 0 0 1
## $5,000 to $7,499 0 0 1 1
## $7,500 to $9,999 1 0 1 2
## $10,000 to $12,499 3 0 0 1
## $12,500 to $14,999 6 0 1 0
## $15,000 to $19,999 2 0 0 0
## $20,000 to $24,999 10 4 0 1
## $25,000 to $29,999 6 0 2 1
## $30,000 to $34,999 3 0 1 0
## $35,000 to $39,999 7 1 4 5
## $40,000 to $49,999 4 0 1 2
## $50,000 to $59,999 13 1 6 6
## $60,000 to $74,999 19 5 6 3
## $75,000 to $84,999 11 1 3 3
## $85,000 to $99,999 5 0 6 3
## $100,000 to $124,999 14 0 7 8
## $125,000 to $149,999 7 0 5 2
## $150,000 to $174,999 10 2 5 4
## $175,000 or more 6 3 4 3
## Q10_q
## PPINCIMP Q10_5_Taxi Q10_6_Airplane Q10_7_Don_t.know
## Less than $5,000 1 0 0
## $5,000 to $7,499 0 0 0
## $7,500 to $9,999 1 0 0
## $10,000 to $12,499 1 1 0
## $12,500 to $14,999 0 0 0
## $15,000 to $19,999 0 0 0
## $20,000 to $24,999 3 1 0
## $25,000 to $29,999 0 0 0
## $30,000 to $34,999 0 0 0
## $35,000 to $39,999 1 1 0
## $40,000 to $49,999 0 0 0
## $50,000 to $59,999 4 2 0
## $60,000 to $74,999 0 0 0
## $75,000 to $84,999 0 0 1
## $85,000 to $99,999 2 1 0
## $100,000 to $124,999 3 2 0
## $125,000 to $149,999 1 1 0
## $150,000 to $174,999 6 5 0
## $175,000 or more 3 5 0
## Q10_q
## PPINCIMP Q10_8_Other
## Less than $5,000 0
## $5,000 to $7,499 1
## $7,500 to $9,999 0
## $10,000 to $12,499 0
## $12,500 to $14,999 0
## $15,000 to $19,999 0
## $20,000 to $24,999 2
## $25,000 to $29,999 1
## $30,000 to $34,999 1
## $35,000 to $39,999 0
## $40,000 to $49,999 0
## $50,000 to $59,999 0
## $60,000 to $74,999 1
## $75,000 to $84,999 0
## $85,000 to $99,999 0
## $100,000 to $124,999 2
## $125,000 to $149,999 0
## $150,000 to $174,999 2
## $175,000 or more 1Q11. How do you rate your risk of getting influenza if you visited each of the following locations?
q11_long <- data2 %>%
gather("Q11_q", "Q11_r", starts_with("Q11_"), -contains("Text"), -contains("Refused"), na.rm = TRUE)
with(q11_long, table(Q11_q, Q11_r))## Q11_r
## Q11_q Don_t Know High Risk, Very Likely
## Q11_1_Work 185 524
## Q11_10_Family.or.friends 121 541
## Q11_11_Other 915 51
## Q11_2_Schools 178 909
## Q11_3_Day.care 214 924
## Q11_4_Stores 115 551
## Q11_5_Restaurants 111 483
## Q11_6_Libraries 169 386
## Q11_7_Hospitals 123 982
## Q11_8_Doctor_s.office 110 994
## Q11_9_Public.transportation 147 1093
## Q11_r
## Q11_q Low Risk, Not Likely
## Q11_1_Work 643
## Q11_10_Family.or.friends 485
## Q11_11_Other 104
## Q11_2_Schools 508
## Q11_3_Day.care 554
## Q11_4_Stores 405
## Q11_5_Restaurants 442
## Q11_6_Libraries 700
## Q11_7_Hospitals 374
## Q11_8_Doctor_s.office 308
## Q11_9_Public.transportation 353
## Q11_r
## Q11_q Medium Risk, Somewhat Likely
## Q11_1_Work 795
## Q11_10_Family.or.friends 1000
## Q11_11_Other 54
## Q11_2_Schools 551
## Q11_3_Day.care 454
## Q11_4_Stores 1076
## Q11_5_Restaurants 1111
## Q11_6_Libraries 890
## Q11_7_Hospitals 669
## Q11_8_Doctor_s.office 733
## Q11_9_Public.transportation 551q11 <- q11_long %>%
count(Q11_q, Q11_r)
ggplot(q11[!is.na(q11$Q11_r), ], aes(x = Q11_r, y = n, fill = Q11_r)) +
geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~Q11_q) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# by gender
with(q11_long, table(PPGENDER, Q11_r, Q11_q))## , , Q11_q = Q11_1_Work
##
## Q11_r
## PPGENDER Don_t Know High Risk, Very Likely Low Risk, Not Likely
## Female 89 309 310
## Male 96 215 333
## Q11_r
## PPGENDER Medium Risk, Somewhat Likely
## Female 381
## Male 414
##
## , , Q11_q = Q11_10_Family.or.friends
##
## Q11_r
## PPGENDER Don_t Know High Risk, Very Likely Low Risk, Not Likely
## Female 53 302 229
## Male 68 239 256
## Q11_r
## PPGENDER Medium Risk, Somewhat Likely
## Female 506
## Male 494
##
## , , Q11_q = Q11_11_Other
##
## Q11_r
## PPGENDER Don_t Know High Risk, Very Likely Low Risk, Not Likely
## Female 449 21 53
## Male 466 30 51
## Q11_r
## PPGENDER Medium Risk, Somewhat Likely
## Female 27
## Male 27
##
## , , Q11_q = Q11_2_Schools
##
## Q11_r
## PPGENDER Don_t Know High Risk, Very Likely Low Risk, Not Likely
## Female 75 500 254
## Male 103 409 254
## Q11_r
## PPGENDER Medium Risk, Somewhat Likely
## Female 259
## Male 292
##
## , , Q11_q = Q11_3_Day.care
##
## Q11_r
## PPGENDER Don_t Know High Risk, Very Likely Low Risk, Not Likely
## Female 94 498 274
## Male 120 426 280
## Q11_r
## PPGENDER Medium Risk, Somewhat Likely
## Female 222
## Male 232
##
## , , Q11_q = Q11_4_Stores
##
## Q11_r
## PPGENDER Don_t Know High Risk, Very Likely Low Risk, Not Likely
## Female 45 285 206
## Male 70 266 199
## Q11_r
## PPGENDER Medium Risk, Somewhat Likely
## Female 553
## Male 523
##
## , , Q11_q = Q11_5_Restaurants
##
## Q11_r
## PPGENDER Don_t Know High Risk, Very Likely Low Risk, Not Likely
## Female 45 266 234
## Male 66 217 208
## Q11_r
## PPGENDER Medium Risk, Somewhat Likely
## Female 544
## Male 567
##
## , , Q11_q = Q11_6_Libraries
##
## Q11_r
## PPGENDER Don_t Know High Risk, Very Likely Low Risk, Not Likely
## Female 65 213 361
## Male 104 173 339
## Q11_r
## PPGENDER Medium Risk, Somewhat Likely
## Female 450
## Male 440
##
## , , Q11_q = Q11_7_Hospitals
##
## Q11_r
## PPGENDER Don_t Know High Risk, Very Likely Low Risk, Not Likely
## Female 52 524 179
## Male 71 458 195
## Q11_r
## PPGENDER Medium Risk, Somewhat Likely
## Female 335
## Male 334
##
## , , Q11_q = Q11_8_Doctor_s.office
##
## Q11_r
## PPGENDER Don_t Know High Risk, Very Likely Low Risk, Not Likely
## Female 41 544 138
## Male 69 450 170
## Q11_r
## PPGENDER Medium Risk, Somewhat Likely
## Female 365
## Male 368
##
## , , Q11_q = Q11_9_Public.transportation
##
## Q11_r
## PPGENDER Don_t Know High Risk, Very Likely Low Risk, Not Likely
## Female 61 575 173
## Male 86 518 180
## Q11_r
## PPGENDER Medium Risk, Somewhat Likely
## Female 279
## Male 272q11 <- q11_long %>%
group_by(PPGENDER, Q11_q, Q11_r) %>%
count(PPGENDER, Q11_q, Q11_r)
ggplot(q11[!is.na(q11$Q11_r), ], aes(x = Q11_r, y = n, fill = PPGENDER)) +
geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~Q11_q) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# by ethnicity
with(q11_long, table(PPETHM, Q11_r, Q11_q))## , , Q11_q = Q11_1_Work
##
## Q11_r
## PPETHM Don_t Know
## White, Non-Hispanic 119
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 6
## Hispanic 27
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM High Risk, Very Likely
## White, Non-Hispanic 381
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 22
## Hispanic 59
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Low Risk, Not Likely
## White, Non-Hispanic 468
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 25
## Hispanic 61
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Medium Risk, Somewhat Likely
## White, Non-Hispanic 586
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 38
## Hispanic 83
## 2+ races, Non-Hispanic 0
##
## , , Q11_q = Q11_10_Family.or.friends
##
## Q11_r
## PPETHM Don_t Know
## White, Non-Hispanic 65
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 10
## Hispanic 20
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM High Risk, Very Likely
## White, Non-Hispanic 379
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 21
## Hispanic 68
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Low Risk, Not Likely
## White, Non-Hispanic 355
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 26
## Hispanic 37
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Medium Risk, Somewhat Likely
## White, Non-Hispanic 755
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 34
## Hispanic 105
## 2+ races, Non-Hispanic 0
##
## , , Q11_q = Q11_11_Other
##
## Q11_r
## PPETHM Don_t Know
## White, Non-Hispanic 655
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 35
## Hispanic 103
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM High Risk, Very Likely
## White, Non-Hispanic 34
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 2
## Hispanic 10
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Low Risk, Not Likely
## White, Non-Hispanic 81
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 1
## Hispanic 11
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Medium Risk, Somewhat Likely
## White, Non-Hispanic 33
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 1
## Hispanic 8
## 2+ races, Non-Hispanic 0
##
## , , Q11_q = Q11_2_Schools
##
## Q11_r
## PPETHM Don_t Know
## White, Non-Hispanic 114
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 9
## Hispanic 22
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM High Risk, Very Likely
## White, Non-Hispanic 664
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 45
## Hispanic 105
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Low Risk, Not Likely
## White, Non-Hispanic 367
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 15
## Hispanic 43
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Medium Risk, Somewhat Likely
## White, Non-Hispanic 409
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 22
## Hispanic 59
## 2+ races, Non-Hispanic 0
##
## , , Q11_q = Q11_3_Day.care
##
## Q11_r
## PPETHM Don_t Know
## White, Non-Hispanic 140
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 9
## Hispanic 34
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM High Risk, Very Likely
## White, Non-Hispanic 680
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 50
## Hispanic 98
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Low Risk, Not Likely
## White, Non-Hispanic 400
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 15
## Hispanic 46
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Medium Risk, Somewhat Likely
## White, Non-Hispanic 334
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 17
## Hispanic 51
## 2+ races, Non-Hispanic 0
##
## , , Q11_q = Q11_4_Stores
##
## Q11_r
## PPETHM Don_t Know
## White, Non-Hispanic 61
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 9
## Hispanic 21
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM High Risk, Very Likely
## White, Non-Hispanic 382
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 22
## Hispanic 74
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Low Risk, Not Likely
## White, Non-Hispanic 297
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 22
## Hispanic 33
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Medium Risk, Somewhat Likely
## White, Non-Hispanic 815
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 38
## Hispanic 101
## 2+ races, Non-Hispanic 0
##
## , , Q11_q = Q11_5_Restaurants
##
## Q11_r
## PPETHM Don_t Know
## White, Non-Hispanic 61
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 8
## Hispanic 18
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM High Risk, Very Likely
## White, Non-Hispanic 326
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 21
## Hispanic 70
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Low Risk, Not Likely
## White, Non-Hispanic 323
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 21
## Hispanic 33
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Medium Risk, Somewhat Likely
## White, Non-Hispanic 845
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 41
## Hispanic 108
## 2+ races, Non-Hispanic 0
##
## , , Q11_q = Q11_6_Libraries
##
## Q11_r
## PPETHM Don_t Know
## White, Non-Hispanic 110
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 8
## Hispanic 22
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM High Risk, Very Likely
## White, Non-Hispanic 263
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 16
## Hispanic 57
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Low Risk, Not Likely
## White, Non-Hispanic 521
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 26
## Hispanic 53
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Medium Risk, Somewhat Likely
## White, Non-Hispanic 659
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 41
## Hispanic 97
## 2+ races, Non-Hispanic 0
##
## , , Q11_q = Q11_7_Hospitals
##
## Q11_r
## PPETHM Don_t Know
## White, Non-Hispanic 78
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 6
## Hispanic 16
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM High Risk, Very Likely
## White, Non-Hispanic 699
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 48
## Hispanic 118
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Low Risk, Not Likely
## White, Non-Hispanic 266
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 12
## Hispanic 34
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Medium Risk, Somewhat Likely
## White, Non-Hispanic 512
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 25
## Hispanic 62
## 2+ races, Non-Hispanic 0
##
## , , Q11_q = Q11_8_Doctor_s.office
##
## Q11_r
## PPETHM Don_t Know
## White, Non-Hispanic 67
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 6
## Hispanic 15
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM High Risk, Very Likely
## White, Non-Hispanic 737
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 39
## Hispanic 108
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Low Risk, Not Likely
## White, Non-Hispanic 217
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 9
## Hispanic 28
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Medium Risk, Somewhat Likely
## White, Non-Hispanic 532
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 37
## Hispanic 78
## 2+ races, Non-Hispanic 0
##
## , , Q11_q = Q11_9_Public.transportation
##
## Q11_r
## PPETHM Don_t Know
## White, Non-Hispanic 91
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 8
## Hispanic 20
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM High Risk, Very Likely
## White, Non-Hispanic 797
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 51
## Hispanic 124
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Low Risk, Not Likely
## White, Non-Hispanic 259
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 11
## Hispanic 27
## 2+ races, Non-Hispanic 0
## Q11_r
## PPETHM Medium Risk, Somewhat Likely
## White, Non-Hispanic 406
## Black or African American, Non-Hispanic 0
## Other, Non-Hispanic 21
## Hispanic 57
## 2+ races, Non-Hispanic 0q11 <- q11_long %>%
group_by(PPETHM, Q11_q, Q11_r) %>%
count(PPETHM, Q11_q, Q11_r)
ggplot(q11[!is.na(q11$Q11_r), ], aes(x = Q11_r, y = n, fill = PPETHM)) +
geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~Q11_q) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# by income
with(q11_long, table(PPINCIMP, Q11_q, Q11_r))## , , Q11_r = Don_t Know
##
## Q11_q
## PPINCIMP Q11_1_Work Q11_10_Family.or.friends Q11_11_Other
## Less than $5,000 14 14 22
## $5,000 to $7,499 7 5 15
## $7,500 to $9,999 5 2 4
## $10,000 to $12,499 4 4 25
## $12,500 to $14,999 8 8 20
## $15,000 to $19,999 12 8 29
## $20,000 to $24,999 10 7 27
## $25,000 to $29,999 10 3 41
## $30,000 to $34,999 11 6 42
## $35,000 to $39,999 10 6 39
## $40,000 to $49,999 9 8 71
## $50,000 to $59,999 16 7 75
## $60,000 to $74,999 14 10 91
## $75,000 to $84,999 14 10 73
## $85,000 to $99,999 7 3 58
## $100,000 to $124,999 17 9 146
## $125,000 to $149,999 6 4 61
## $150,000 to $174,999 4 1 25
## $175,000 or more 7 6 51
## Q11_q
## PPINCIMP Q11_2_Schools Q11_3_Day.care Q11_4_Stores
## Less than $5,000 15 14 12
## $5,000 to $7,499 5 6 5
## $7,500 to $9,999 4 4 3
## $10,000 to $12,499 3 4 2
## $12,500 to $14,999 7 10 6
## $15,000 to $19,999 12 12 10
## $20,000 to $24,999 7 8 5
## $25,000 to $29,999 9 10 3
## $30,000 to $34,999 10 11 6
## $35,000 to $39,999 12 15 6
## $40,000 to $49,999 11 10 7
## $50,000 to $59,999 14 19 6
## $60,000 to $74,999 18 19 12
## $75,000 to $84,999 12 17 8
## $85,000 to $99,999 5 6 3
## $100,000 to $124,999 18 27 12
## $125,000 to $149,999 6 7 4
## $150,000 to $174,999 3 4 1
## $175,000 or more 7 11 4
## Q11_q
## PPINCIMP Q11_5_Restaurants Q11_6_Libraries Q11_7_Hospitals
## Less than $5,000 13 15 15
## $5,000 to $7,499 5 5 5
## $7,500 to $9,999 3 3 3
## $10,000 to $12,499 2 3 3
## $12,500 to $14,999 6 7 4
## $15,000 to $19,999 8 12 9
## $20,000 to $24,999 4 6 5
## $25,000 to $29,999 3 8 6
## $30,000 to $34,999 7 8 7
## $35,000 to $39,999 7 11 8
## $40,000 to $49,999 5 8 6
## $50,000 to $59,999 9 14 10
## $60,000 to $74,999 10 13 12
## $75,000 to $84,999 5 12 4
## $85,000 to $99,999 2 5 2
## $100,000 to $124,999 13 24 12
## $125,000 to $149,999 4 6 4
## $150,000 to $174,999 1 3 1
## $175,000 or more 4 6 7
## Q11_q
## PPINCIMP Q11_8_Doctor_s.office Q11_9_Public.transportation
## Less than $5,000 14 14
## $5,000 to $7,499 5 5
## $7,500 to $9,999 3 4
## $10,000 to $12,499 0 2
## $12,500 to $14,999 3 8
## $15,000 to $19,999 9 9
## $20,000 to $24,999 4 5
## $25,000 to $29,999 5 7
## $30,000 to $34,999 8 7
## $35,000 to $39,999 5 10
## $40,000 to $49,999 6 9
## $50,000 to $59,999 10 16
## $60,000 to $74,999 9 13
## $75,000 to $84,999 6 7
## $85,000 to $99,999 1 3
## $100,000 to $124,999 9 16
## $125,000 to $149,999 4 4
## $150,000 to $174,999 1 1
## $175,000 or more 8 7
##
## , , Q11_r = High Risk, Very Likely
##
## Q11_q
## PPINCIMP Q11_1_Work Q11_10_Family.or.friends Q11_11_Other
## Less than $5,000 11 11 2
## $5,000 to $7,499 5 5 0
## $7,500 to $9,999 1 4 1
## $10,000 to $12,499 14 18 2
## $12,500 to $14,999 11 11 3
## $15,000 to $19,999 14 11 1
## $20,000 to $24,999 12 19 2
## $25,000 to $29,999 33 24 4
## $30,000 to $34,999 24 20 2
## $35,000 to $39,999 32 33 5
## $40,000 to $49,999 37 41 1
## $50,000 to $59,999 33 52 2
## $60,000 to $74,999 61 64 5
## $75,000 to $84,999 43 39 3
## $85,000 to $99,999 40 33 3
## $100,000 to $124,999 86 69 10
## $125,000 to $149,999 26 35 1
## $150,000 to $174,999 21 25 2
## $175,000 or more 20 27 2
## Q11_q
## PPINCIMP Q11_2_Schools Q11_3_Day.care Q11_4_Stores
## Less than $5,000 18 19 13
## $5,000 to $7,499 6 7 6
## $7,500 to $9,999 3 3 4
## $10,000 to $12,499 24 24 21
## $12,500 to $14,999 19 16 17
## $15,000 to $19,999 28 28 18
## $20,000 to $24,999 28 28 16
## $25,000 to $29,999 45 43 31
## $30,000 to $34,999 34 34 24
## $35,000 to $39,999 44 38 32
## $40,000 to $49,999 59 59 40
## $50,000 to $59,999 81 81 45
## $60,000 to $74,999 87 88 51
## $75,000 to $84,999 69 68 48
## $85,000 to $99,999 68 69 38
## $100,000 to $124,999 143 145 60
## $125,000 to $149,999 55 67 34
## $150,000 to $174,999 41 44 23
## $175,000 or more 57 63 30
## Q11_q
## PPINCIMP Q11_5_Restaurants Q11_6_Libraries Q11_7_Hospitals
## Less than $5,000 11 10 19
## $5,000 to $7,499 6 5 7
## $7,500 to $9,999 3 3 5
## $10,000 to $12,499 15 14 25
## $12,500 to $14,999 13 10 24
## $15,000 to $19,999 16 11 30
## $20,000 to $24,999 14 10 34
## $25,000 to $29,999 29 20 44
## $30,000 to $34,999 22 21 36
## $35,000 to $39,999 29 26 47
## $40,000 to $49,999 34 24 66
## $50,000 to $59,999 33 32 79
## $60,000 to $74,999 52 40 96
## $75,000 to $84,999 45 30 74
## $85,000 to $99,999 34 30 73
## $100,000 to $124,999 53 38 147
## $125,000 to $149,999 26 23 69
## $150,000 to $174,999 19 15 42
## $175,000 or more 29 24 65
## Q11_q
## PPINCIMP Q11_8_Doctor_s.office Q11_9_Public.transportation
## Less than $5,000 20 21
## $5,000 to $7,499 7 9
## $7,500 to $9,999 3 4
## $10,000 to $12,499 26 22
## $12,500 to $14,999 21 20
## $15,000 to $19,999 29 30
## $20,000 to $24,999 34 32
## $25,000 to $29,999 45 54
## $30,000 to $34,999 40 39
## $35,000 to $39,999 43 53
## $40,000 to $49,999 74 71
## $50,000 to $59,999 80 92
## $60,000 to $74,999 105 113
## $75,000 to $84,999 72 90
## $85,000 to $99,999 74 80
## $100,000 to $124,999 151 165
## $125,000 to $149,999 66 77
## $150,000 to $174,999 39 50
## $175,000 or more 65 71
##
## , , Q11_r = Low Risk, Not Likely
##
## Q11_q
## PPINCIMP Q11_1_Work Q11_10_Family.or.friends Q11_11_Other
## Less than $5,000 14 11 3
## $5,000 to $7,499 6 2 1
## $7,500 to $9,999 5 2 1
## $10,000 to $12,499 20 11 5
## $12,500 to $14,999 13 10 3
## $15,000 to $19,999 15 15 0
## $20,000 to $24,999 20 11 3
## $25,000 to $29,999 27 22 8
## $30,000 to $34,999 26 24 6
## $35,000 to $39,999 26 20 7
## $40,000 to $49,999 52 35 12
## $50,000 to $59,999 59 43 7
## $60,000 to $74,999 66 39 11
## $75,000 to $84,999 46 32 3
## $85,000 to $99,999 53 41 9
## $100,000 to $124,999 90 88 14
## $125,000 to $149,999 44 27 4
## $150,000 to $174,999 24 20 3
## $175,000 or more 37 32 4
## Q11_q
## PPINCIMP Q11_2_Schools Q11_3_Day.care Q11_4_Stores
## Less than $5,000 7 6 6
## $5,000 to $7,499 4 4 4
## $7,500 to $9,999 3 3 1
## $10,000 to $12,499 19 19 12
## $12,500 to $14,999 12 13 6
## $15,000 to $19,999 8 13 10
## $20,000 to $24,999 12 14 11
## $25,000 to $29,999 25 29 22
## $30,000 to $34,999 29 31 17
## $35,000 to $39,999 31 32 15
## $40,000 to $49,999 40 51 32
## $50,000 to $59,999 45 48 36
## $60,000 to $74,999 66 69 40
## $75,000 to $84,999 33 34 27
## $85,000 to $99,999 38 41 26
## $100,000 to $124,999 73 77 68
## $125,000 to $149,999 22 27 31
## $150,000 to $174,999 16 17 17
## $175,000 or more 25 26 24
## Q11_q
## PPINCIMP Q11_5_Restaurants Q11_6_Libraries Q11_7_Hospitals
## Less than $5,000 4 10 6
## $5,000 to $7,499 3 5 2
## $7,500 to $9,999 2 0 1
## $10,000 to $12,499 12 18 10
## $12,500 to $14,999 6 13 10
## $15,000 to $19,999 11 19 5
## $20,000 to $24,999 14 22 12
## $25,000 to $29,999 23 35 23
## $30,000 to $34,999 19 31 19
## $35,000 to $39,999 20 27 26
## $40,000 to $49,999 33 56 34
## $50,000 to $59,999 42 61 29
## $60,000 to $74,999 38 77 38
## $75,000 to $84,999 25 54 29
## $85,000 to $99,999 31 43 24
## $100,000 to $124,999 74 115 60
## $125,000 to $149,999 37 41 17
## $150,000 to $174,999 21 32 15
## $175,000 or more 27 41 14
## Q11_q
## PPINCIMP Q11_8_Doctor_s.office Q11_9_Public.transportation
## Less than $5,000 5 6
## $5,000 to $7,499 2 2
## $7,500 to $9,999 1 0
## $10,000 to $12,499 8 16
## $12,500 to $14,999 11 9
## $15,000 to $19,999 6 9
## $20,000 to $24,999 9 7
## $25,000 to $29,999 19 20
## $30,000 to $34,999 15 25
## $35,000 to $39,999 23 18
## $40,000 to $49,999 24 31
## $50,000 to $59,999 28 34
## $60,000 to $74,999 34 42
## $75,000 to $84,999 27 23
## $85,000 to $99,999 21 26
## $100,000 to $124,999 41 46
## $125,000 to $149,999 14 13
## $150,000 to $174,999 10 8
## $175,000 or more 10 18
##
## , , Q11_r = Medium Risk, Somewhat Likely
##
## Q11_q
## PPINCIMP Q11_1_Work Q11_10_Family.or.friends Q11_11_Other
## Less than $5,000 13 16 2
## $5,000 to $7,499 7 13 2
## $7,500 to $9,999 2 5 1
## $10,000 to $12,499 16 22 0
## $12,500 to $14,999 15 18 0
## $15,000 to $19,999 22 29 2
## $20,000 to $24,999 29 34 2
## $25,000 to $29,999 29 50 2
## $30,000 to $34,999 30 41 2
## $35,000 to $39,999 36 45 6
## $40,000 to $49,999 49 63 3
## $50,000 to $59,999 74 80 4
## $60,000 to $74,999 81 109 7
## $75,000 to $84,999 56 78 3
## $85,000 to $99,999 52 75 8
## $100,000 to $124,999 132 158 4
## $125,000 to $149,999 56 66 2
## $150,000 to $174,999 35 38 3
## $175,000 or more 61 60 1
## Q11_q
## PPINCIMP Q11_2_Schools Q11_3_Day.care Q11_4_Stores
## Less than $5,000 12 13 21
## $5,000 to $7,499 10 8 10
## $7,500 to $9,999 3 3 5
## $10,000 to $12,499 9 8 20
## $12,500 to $14,999 9 8 18
## $15,000 to $19,999 15 10 25
## $20,000 to $24,999 24 21 39
## $25,000 to $29,999 20 17 43
## $30,000 to $34,999 17 14 43
## $35,000 to $39,999 17 19 51
## $40,000 to $49,999 37 27 68
## $50,000 to $59,999 42 34 95
## $60,000 to $74,999 50 45 119
## $75,000 to $84,999 45 40 76
## $85,000 to $99,999 41 36 85
## $100,000 to $124,999 91 76 185
## $125,000 to $149,999 49 31 63
## $150,000 to $174,999 24 19 43
## $175,000 or more 36 25 67
## Q11_q
## PPINCIMP Q11_5_Restaurants Q11_6_Libraries Q11_7_Hospitals
## Less than $5,000 24 17 12
## $5,000 to $7,499 11 10 11
## $7,500 to $9,999 6 6 3
## $10,000 to $12,499 25 21 18
## $12,500 to $14,999 22 17 9
## $15,000 to $19,999 28 21 19
## $20,000 to $24,999 39 33 20
## $25,000 to $29,999 44 36 26
## $30,000 to $34,999 42 30 29
## $35,000 to $39,999 48 40 23
## $40,000 to $49,999 75 59 41
## $50,000 to $59,999 98 75 64
## $60,000 to $74,999 122 91 76
## $75,000 to $84,999 84 63 52
## $85,000 to $99,999 85 74 53
## $100,000 to $124,999 185 147 106
## $125,000 to $149,999 65 62 42
## $150,000 to $174,999 43 34 26
## $175,000 or more 65 54 39
## Q11_q
## PPINCIMP Q11_8_Doctor_s.office Q11_9_Public.transportation
## Less than $5,000 13 11
## $5,000 to $7,499 11 9
## $7,500 to $9,999 5 4
## $10,000 to $12,499 20 13
## $12,500 to $14,999 12 10
## $15,000 to $19,999 19 15
## $20,000 to $24,999 24 27
## $25,000 to $29,999 30 18
## $30,000 to $34,999 27 19
## $35,000 to $39,999 33 23
## $40,000 to $49,999 43 36
## $50,000 to $59,999 64 40
## $60,000 to $74,999 74 54
## $75,000 to $84,999 54 39
## $85,000 to $99,999 56 43
## $100,000 to $124,999 124 98
## $125,000 to $149,999 48 38
## $150,000 to $174,999 34 25
## $175,000 or more 42 29q11 <- q11_long %>%
group_by(PPINCIMP, Q11_q, Q11_r) %>%
count(PPINCIMP, Q11_q, Q11_r)
ggplot(q11[!is.na(q11$Q11_r), ], aes(x = Q11_r, y = n, fill = PPINCIMP)) +
geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~Q11_q) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Q12. Which of the following actions do you take to avoid getting sick?
Q12 <- data2 %>%
select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 75:91) %>%
gather("q", "r", 7:21)
with(Q12, table(q, r))## r
## q Always Never
## Q12_1_Avoid.touching.my.eyes 653 324
## Q12_10_Get.recommended.vaccine 1041 564
## Q12_11_Take.preventive.medicine 425 831
## Q12_12_Cover.my.nose.and.mouth.with.a.surgical.mask 218 1568
## Q12_13_Avoid.contact.with.people.who.are.sick 765 153
## Q12_14_Avoid.crowded.places 406 413
## Q12_15_Other 91 472
## Q12_2_Avoid.touching.my.nose 613 349
## Q12_3_Avoid.touching.my.mouth 758 300
## Q12_4_Wash.my.hands.with.soap.more.often 1774 52
## Q12_5_Use.hand.sanitizers 911 278
## Q12_6_Clean.the.surfaces.in.my.home 1132 115
## Q12_7_Clean.the.surfaces.at.work 752 544
## Q12_8_Eat.nutritious.food 895 107
## Q12_9_Get.adequate.rest 899 114
## r
## q Sometimes
## Q12_1_Avoid.touching.my.eyes 1168
## Q12_10_Get.recommended.vaccine 540
## Q12_11_Take.preventive.medicine 890
## Q12_12_Cover.my.nose.and.mouth.with.a.surgical.mask 358
## Q12_13_Avoid.contact.with.people.who.are.sick 1228
## Q12_14_Avoid.crowded.places 1322
## Q12_15_Other 87
## Q12_2_Avoid.touching.my.nose 1183
## Q12_3_Avoid.touching.my.mouth 1085
## Q12_4_Wash.my.hands.with.soap.more.often 317
## Q12_5_Use.hand.sanitizers 957
## Q12_6_Clean.the.surfaces.in.my.home 899
## Q12_7_Clean.the.surfaces.at.work 842
## Q12_8_Eat.nutritious.food 1144
## Q12_9_Get.adequate.rest 1130q12 <- Q12 %>%
count(q, r)Q13. Do you get the flu vaccine?
with(data2, table(Q13))## Q13
## Yes, every year Yes, some years No, never
## 908 423 819ggplot(data2[!is.na(data2$Q13), ]) + geom_bar(mapping = aes(x = Q13, fill = Q13), position = position_dodge())
# by gender
with(data2, table(Q13, PPGENDER))## PPGENDER
## Q13 Female Male
## Yes, every year 460 448
## Yes, some years 227 196
## No, never 408 411q13 <- data2 %>%
count(Q13, PPGENDER)
ggplot(q13[!is.na(q13$Q13), ], aes(x = Q13, y = n, fill = PPGENDER)) +
geom_bar(stat = 'identity', position = position_dodge())
# by eth
with(data2, table(Q13, PPINCIMP))## PPINCIMP
## Q13 Less than $5,000 $5,000 to $7,499 $7,500 to $9,999
## Yes, every year 15 4 5
## Yes, some years 4 9 2
## No, never 33 11 6
## PPINCIMP
## Q13 $10,000 to $12,499 $12,500 to $14,999 $15,000 to $19,999
## Yes, every year 21 15 23
## Yes, some years 11 9 5
## No, never 24 23 34
## PPINCIMP
## Q13 $20,000 to $24,999 $25,000 to $29,999 $30,000 to $34,999
## Yes, every year 24 38 35
## Yes, some years 11 18 21
## No, never 37 42 36
## PPINCIMP
## Q13 $35,000 to $39,999 $40,000 to $49,999 $50,000 to $59,999
## Yes, every year 41 64 80
## Yes, some years 17 25 33
## No, never 46 59 70
## PPINCIMP
## Q13 $60,000 to $74,999 $75,000 to $84,999 $85,000 to $99,999
## Yes, every year 88 55 76
## Yes, some years 45 40 34
## No, never 88 64 42
## PPINCIMP
## Q13 $100,000 to $124,999 $125,000 to $149,999
## Yes, every year 148 65
## Yes, some years 70 29
## No, never 108 38
## PPINCIMP
## Q13 $150,000 to $174,999 $175,000 or more
## Yes, every year 45 66
## Yes, some years 15 25
## No, never 24 34q13 <- data2 %>%
count(Q13, PPINCIMP)
ggplot(q13[!is.na(q13$Q13), ], aes(x = Q13, y = n, fill = PPINCIMP)) +
geom_bar(stat = 'identity', position = position_dodge())
Q14. How much do you pay to get an influenza vaccine?
with(data2, table(Q14))## Q14
## $0 Less than $30 $30 to $60 More than $60 Don_t know
## 970 222 54 4 80ggplot(data2[!is.na(data2$Q14), ]) + geom_bar(mapping = aes(x = Q14, fill = Q14), position = position_dodge())
# by gender
with(data2, by(Q14, PPGENDER, summary))## PPGENDER: Female
## $0 Less than $30 $30 to $60 More than $60 Don_t know
## 514 101 28 2 41
## NA's
## 411
## --------------------------------------------------------
## PPGENDER: Male
## $0 Less than $30 $30 to $60 More than $60 Don_t know
## 456 121 26 2 39
## NA's
## 427Q15. Are you more likely to get a vaccine if others around you get a vaccine?
with(data2, table(Q15))## Q15
## No, less likely No, no effect Yes, more likely
## 70 878 381ggplot(data2[!is.na(data2$Q15), ]) + geom_bar(mapping = aes(x = Q15, fill = Q15), position = position_dodge())
#
with(data2, table(Q15, PPGENDER))## PPGENDER
## Q15 Female Male
## No, less likely 31 39
## No, no effect 477 401
## Yes, more likely 179 202#
with(data2, table(Q15, PPETHM))## PPETHM
## Q15 White, Non-Hispanic
## No, less likely 32
## No, no effect 708
## Yes, more likely 248
## PPETHM
## Q15 Black or African American, Non-Hispanic
## No, less likely 0
## No, no effect 0
## Yes, more likely 0
## PPETHM
## Q15 Other, Non-Hispanic Hispanic 2+ races, Non-Hispanic
## No, less likely 5 14 0
## No, no effect 27 69 0
## Yes, more likely 32 51 0#
with(data2, table(PPINCIMP, Q15))## Q15
## PPINCIMP No, less likely No, no effect Yes, more likely
## Less than $5,000 3 6 10
## $5,000 to $7,499 2 6 5
## $7,500 to $9,999 1 4 2
## $10,000 to $12,499 5 16 11
## $12,500 to $14,999 0 13 10
## $15,000 to $19,999 1 15 12
## $20,000 to $24,999 0 25 10
## $25,000 to $29,999 4 34 17
## $30,000 to $34,999 2 39 15
## $35,000 to $39,999 3 38 17
## $40,000 to $49,999 5 58 26
## $50,000 to $59,999 8 78 27
## $60,000 to $74,999 9 85 39
## $75,000 to $84,999 4 67 24
## $85,000 to $99,999 5 79 26
## $100,000 to $124,999 8 151 59
## $125,000 to $149,999 6 60 28
## $150,000 to $174,999 3 37 20
## $175,000 or more 1 67 23Q16. Are you more likely to get a vaccine if others around you do not get a vaccine?
with(data2, table(Q16))## Q16
## No, less likely No, no effect Yes, more likely
## 101 904 313ggplot(data2[!is.na(data2$Q16), ]) + geom_bar(mapping = aes(x = Q16, fill = Q16), position = position_dodge())
#
with(data2, table(Q16, PPGENDER))## PPGENDER
## Q16 Female Male
## No, less likely 43 58
## No, no effect 472 432
## Yes, more likely 162 151#
with(data2, table(Q16, PPETHM))## PPETHM
## Q16 White, Non-Hispanic
## No, less likely 58
## No, no effect 721
## Yes, more likely 198
## PPETHM
## Q16 Black or African American, Non-Hispanic
## No, less likely 0
## No, no effect 0
## Yes, more likely 0
## PPETHM
## Q16 Other, Non-Hispanic Hispanic 2+ races, Non-Hispanic
## No, less likely 11 18 0
## No, no effect 30 69 0
## Yes, more likely 22 47 0#
with(data2, table(PPINCIMP, Q16))## Q16
## PPINCIMP No, less likely No, no effect Yes, more likely
## Less than $5,000 2 8 9
## $5,000 to $7,499 3 7 3
## $7,500 to $9,999 0 4 3
## $10,000 to $12,499 3 18 11
## $12,500 to $14,999 4 10 9
## $15,000 to $19,999 0 16 12
## $20,000 to $24,999 4 21 8
## $25,000 to $29,999 2 34 20
## $30,000 to $34,999 3 41 11
## $35,000 to $39,999 5 37 16
## $40,000 to $49,999 9 54 24
## $50,000 to $59,999 9 77 26
## $60,000 to $74,999 9 97 27
## $75,000 to $84,999 8 65 21
## $85,000 to $99,999 7 83 19
## $100,000 to $124,999 13 156 47
## $125,000 to $149,999 11 65 17
## $150,000 to $174,999 5 40 14
## $175,000 or more 4 71 16Q17. Do you get a vaccine to protect yourself, protect others, or protect yourself and others?
with(data2, table(Q17))## Q17
## Protect myself Protect myself and others
## 381 921
## Protect others
## 22ggplot(data2[!is.na(data2$Q17), ]) + geom_bar(mapping = aes(x = Q17, fill = Q17), position = position_dodge())
#
with(data2, table(Q17, PPGENDER))## PPGENDER
## Q17 Female Male
## Protect myself 175 206
## Protect myself and others 500 421
## Protect others 9 13#
with(data2, table(Q17, PPETHM))## PPETHM
## Q17 White, Non-Hispanic
## Protect myself 291
## Protect myself and others 682
## Protect others 13
## PPETHM
## Q17 Black or African American, Non-Hispanic
## Protect myself 0
## Protect myself and others 0
## Protect others 0
## PPETHM
## Q17 Other, Non-Hispanic Hispanic
## Protect myself 15 35
## Protect myself and others 47 93
## Protect others 2 5
## PPETHM
## Q17 2+ races, Non-Hispanic
## Protect myself 0
## Protect myself and others 0
## Protect others 0#
with(data2, table(PPINCIMP, Q17))## Q17
## PPINCIMP Protect myself Protect myself and others
## Less than $5,000 7 10
## $5,000 to $7,499 4 9
## $7,500 to $9,999 2 5
## $10,000 to $12,499 10 21
## $12,500 to $14,999 4 18
## $15,000 to $19,999 11 17
## $20,000 to $24,999 13 21
## $25,000 to $29,999 19 35
## $30,000 to $34,999 16 40
## $35,000 to $39,999 14 43
## $40,000 to $49,999 20 69
## $50,000 to $59,999 29 78
## $60,000 to $74,999 38 90
## $75,000 to $84,999 30 63
## $85,000 to $99,999 31 76
## $100,000 to $124,999 63 153
## $125,000 to $149,999 29 64
## $150,000 to $174,999 14 45
## $175,000 or more 27 64
## Q17
## PPINCIMP Protect others
## Less than $5,000 1
## $5,000 to $7,499 0
## $7,500 to $9,999 0
## $10,000 to $12,499 1
## $12,500 to $14,999 2
## $15,000 to $19,999 0
## $20,000 to $24,999 0
## $25,000 to $29,999 2
## $30,000 to $34,999 0
## $35,000 to $39,999 1
## $40,000 to $49,999 0
## $50,000 to $59,999 4
## $60,000 to $74,999 4
## $75,000 to $84,999 2
## $85,000 to $99,999 3
## $100,000 to $124,999 2
## $125,000 to $149,999 0
## $150,000 to $174,999 0
## $175,000 or more 0Q18. What are the reasons you would not get an influenza vaccine?
Q18 <- data2 %>%
select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 97:108) %>%
gather("q", "r", 7:Q18_10_Other)
with(Q18, table(q, r))## r
## q No
## Q18_1_The.vaccine.costs.too.much 1132
## Q18_10_Other 1064
## Q18_2_The.vaccine.is.not.very.effective.in.preventing.influenza 903
## Q18_3_I.am.not.likely.to.get.influenza 964
## Q18_4_Do.not.know.where.to.get.vaccine 1199
## Q18_5_The.side.effect.of.the.vaccine.are.too.risky 958
## Q18_6_I.am.allergic.to.some.of.the.ingredients.in.the.vaccine 1184
## Q18_7_I.do.not.like.shots 976
## Q18_8_I.just.don_t.get.around.to.doing.it 878
## Q18_9_I.have.to.travel.too.far.to.get.vaccine 1216
## r
## q Yes
## Q18_1_The.vaccine.costs.too.much 110
## Q18_10_Other 178
## Q18_2_The.vaccine.is.not.very.effective.in.preventing.influenza 339
## Q18_3_I.am.not.likely.to.get.influenza 278
## Q18_4_Do.not.know.where.to.get.vaccine 43
## Q18_5_The.side.effect.of.the.vaccine.are.too.risky 284
## Q18_6_I.am.allergic.to.some.of.the.ingredients.in.the.vaccine 58
## Q18_7_I.do.not.like.shots 266
## Q18_8_I.just.don_t.get.around.to.doing.it 364
## Q18_9_I.have.to.travel.too.far.to.get.vaccine 26q18 <- Q18 %>%
count(q, r)Q19. Do you have health insurance?
with(data2, table(Q19))## Q19
## No Yes
## 154 1994ggplot(data2[!is.na(data2$Q19), ]) + geom_bar(mapping = aes(x = Q19, fill = Q19), position = position_dodge())
#
with(data2, table(Q19, PPGENDER))## PPGENDER
## Q19 Female Male
## No 60 94
## Yes 1033 961#
with(data2, table(Q19, PPETHM))## PPETHM
## Q19 White, Non-Hispanic Black or African American, Non-Hispanic
## No 77 0
## Yes 1478 0
## PPETHM
## Q19 Other, Non-Hispanic Hispanic 2+ races, Non-Hispanic
## No 8 40 0
## Yes 85 188 0#
with(data2, table(PPINCIMP, Q19))## Q19
## PPINCIMP No Yes
## Less than $5,000 18 33
## $5,000 to $7,499 5 20
## $7,500 to $9,999 2 12
## $10,000 to $12,499 8 48
## $12,500 to $14,999 10 37
## $15,000 to $19,999 8 54
## $20,000 to $24,999 13 59
## $25,000 to $29,999 7 91
## $30,000 to $34,999 10 80
## $35,000 to $39,999 8 96
## $40,000 to $49,999 10 138
## $50,000 to $59,999 7 176
## $60,000 to $74,999 14 206
## $75,000 to $84,999 9 151
## $85,000 to $99,999 7 146
## $100,000 to $124,999 7 318
## $125,000 to $149,999 4 128
## $150,000 to $174,999 2 81
## $175,000 or more 5 120Q20. How effective do you think the influenza vaccine is in protecting people from becoming sick with influenza?
with(data2, table(Q20))## Q20
## Very effective Somewhat effective
## 383 961
## It varies from season to season Not effective
## 433 144
## Don_t know
## 228ggplot(data2[!is.na(data2$Q20), ]) + geom_bar(mapping = aes(x = Q20, fill = Q20), position = position_dodge())
#
with(data2, table(Q20, PPGENDER))## PPGENDER
## Q20 Female Male
## Very effective 205 178
## Somewhat effective 464 497
## It varies from season to season 243 190
## Not effective 73 71
## Don_t know 110 118#
with(data2, table(Q20, PPETHM))## PPETHM
## Q20 White, Non-Hispanic
## Very effective 280
## Somewhat effective 712
## It varies from season to season 333
## Not effective 94
## Don_t know 135
## PPETHM
## Q20 Black or African American, Non-Hispanic
## Very effective 0
## Somewhat effective 0
## It varies from season to season 0
## Not effective 0
## Don_t know 0
## PPETHM
## Q20 Other, Non-Hispanic Hispanic
## Very effective 14 41
## Somewhat effective 36 101
## It varies from season to season 24 31
## Not effective 4 21
## Don_t know 15 35
## PPETHM
## Q20 2+ races, Non-Hispanic
## Very effective 0
## Somewhat effective 0
## It varies from season to season 0
## Not effective 0
## Don_t know 0#
with(data2, table(PPINCIMP, Q20))## Q20
## PPINCIMP Very effective Somewhat effective
## Less than $5,000 14 9
## $5,000 to $7,499 5 7
## $7,500 to $9,999 2 4
## $10,000 to $12,499 8 28
## $12,500 to $14,999 9 10
## $15,000 to $19,999 10 23
## $20,000 to $24,999 15 26
## $25,000 to $29,999 16 47
## $30,000 to $34,999 14 35
## $35,000 to $39,999 23 37
## $40,000 to $49,999 28 59
## $50,000 to $59,999 38 75
## $60,000 to $74,999 23 106
## $75,000 to $84,999 25 71
## $85,000 to $99,999 26 81
## $100,000 to $124,999 62 165
## $125,000 to $149,999 28 71
## $150,000 to $174,999 20 38
## $175,000 or more 17 69
## Q20
## PPINCIMP It varies from season to season Not effective
## Less than $5,000 6 5
## $5,000 to $7,499 3 0
## $7,500 to $9,999 1 2
## $10,000 to $12,499 9 5
## $12,500 to $14,999 18 5
## $15,000 to $19,999 14 3
## $20,000 to $24,999 14 7
## $25,000 to $29,999 17 6
## $30,000 to $34,999 16 6
## $35,000 to $39,999 19 9
## $40,000 to $49,999 37 11
## $50,000 to $59,999 45 10
## $60,000 to $74,999 45 19
## $75,000 to $84,999 33 14
## $85,000 to $99,999 30 6
## $100,000 to $124,999 60 24
## $125,000 to $149,999 22 2
## $150,000 to $174,999 19 4
## $175,000 or more 25 6
## Q20
## PPINCIMP Don_t know
## Less than $5,000 18
## $5,000 to $7,499 10
## $7,500 to $9,999 5
## $10,000 to $12,499 6
## $12,500 to $14,999 5
## $15,000 to $19,999 12
## $20,000 to $24,999 10
## $25,000 to $29,999 12
## $30,000 to $34,999 19
## $35,000 to $39,999 16
## $40,000 to $49,999 12
## $50,000 to $59,999 15
## $60,000 to $74,999 28
## $75,000 to $84,999 17
## $85,000 to $99,999 10
## $100,000 to $124,999 15
## $125,000 to $149,999 9
## $150,000 to $174,999 2
## $175,000 or more 7Q21. Are influenza vaccines covered by your health insurance?
with(data2, table(Q21))## Q21
## Yes, the full cost is paid
## 1282
## Yes, but only part of the cost is paid
## 153
## No
## 55
## Don_t know
## 500ggplot(data2[!is.na(data2$Q21), ]) + geom_bar(mapping = aes(x = Q21, fill = Q21), position = position_dodge())
#
with(data2, table(Q21, PPGENDER))## PPGENDER
## Q21 Female Male
## Yes, the full cost is paid 670 612
## Yes, but only part of the cost is paid 60 93
## No 31 24
## Don_t know 271 229#
with(data2, table(Q21, PPETHM))## PPETHM
## Q21 White, Non-Hispanic
## Yes, the full cost is paid 945
## Yes, but only part of the cost is paid 112
## No 44
## Don_t know 374
## PPETHM
## Q21 Black or African American, Non-Hispanic
## Yes, the full cost is paid 0
## Yes, but only part of the cost is paid 0
## No 0
## Don_t know 0
## PPETHM
## Q21 Other, Non-Hispanic Hispanic
## Yes, the full cost is paid 46 118
## Yes, but only part of the cost is paid 10 17
## No 2 2
## Don_t know 27 51
## PPETHM
## Q21 2+ races, Non-Hispanic
## Yes, the full cost is paid 0
## Yes, but only part of the cost is paid 0
## No 0
## Don_t know 0#
with(data2, table(PPINCIMP, Q21))## Q21
## PPINCIMP Yes, the full cost is paid
## Less than $5,000 19
## $5,000 to $7,499 13
## $7,500 to $9,999 6
## $10,000 to $12,499 34
## $12,500 to $14,999 21
## $15,000 to $19,999 27
## $20,000 to $24,999 36
## $25,000 to $29,999 56
## $30,000 to $34,999 47
## $35,000 to $39,999 63
## $40,000 to $49,999 82
## $50,000 to $59,999 116
## $60,000 to $74,999 125
## $75,000 to $84,999 98
## $85,000 to $99,999 104
## $100,000 to $124,999 213
## $125,000 to $149,999 89
## $150,000 to $174,999 61
## $175,000 or more 72
## Q21
## PPINCIMP Yes, but only part of the cost is paid No
## Less than $5,000 1 2
## $5,000 to $7,499 0 0
## $7,500 to $9,999 1 1
## $10,000 to $12,499 1 3
## $12,500 to $14,999 3 1
## $15,000 to $19,999 1 2
## $20,000 to $24,999 4 1
## $25,000 to $29,999 7 1
## $30,000 to $34,999 4 4
## $35,000 to $39,999 7 1
## $40,000 to $49,999 18 4
## $50,000 to $59,999 13 6
## $60,000 to $74,999 13 6
## $75,000 to $84,999 12 2
## $85,000 to $99,999 9 2
## $100,000 to $124,999 26 9
## $125,000 to $149,999 9 7
## $150,000 to $174,999 7 1
## $175,000 or more 17 2
## Q21
## PPINCIMP Don_t know
## Less than $5,000 11
## $5,000 to $7,499 6
## $7,500 to $9,999 4
## $10,000 to $12,499 10
## $12,500 to $14,999 12
## $15,000 to $19,999 24
## $20,000 to $24,999 18
## $25,000 to $29,999 27
## $30,000 to $34,999 25
## $35,000 to $39,999 25
## $40,000 to $49,999 34
## $50,000 to $59,999 40
## $60,000 to $74,999 62
## $75,000 to $84,999 39
## $85,000 to $99,999 31
## $100,000 to $124,999 68
## $125,000 to $149,999 23
## $150,000 to $174,999 12
## $175,000 or more 29Q22. Do you do any of the following when you have influenza symptoms?
Q22 <- data2 %>%
select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 112:122) %>%
gather("q", "r", 7:Q22_9_Other)
with(Q22, table(q, r))## r
## q Always
## Q22_1_Go.to.a.doctor_s.office.or.medical.clinic 349
## Q22_2_Decide.on.treatment.without.consulting.a.health.practitioner 335
## Q22_3_Search.the.internet.for.a.treatment 126
## Q22_4_Get.adequate.sleep 1147
## Q22_5_Eat.nutritious.food 909
## Q22_6_Take-over-counter.medication.for.symptoms 796
## Q22_7_Take.an.antiviral.medicine 153
## Q22_8_Take.no.action.to.treat.the.illness 96
## Q22_9_Other 54
## r
## q Never
## Q22_1_Go.to.a.doctor_s.office.or.medical.clinic 552
## Q22_2_Decide.on.treatment.without.consulting.a.health.practitioner 473
## Q22_3_Search.the.internet.for.a.treatment 1148
## Q22_4_Get.adequate.sleep 115
## Q22_5_Eat.nutritious.food 135
## Q22_6_Take-over-counter.medication.for.symptoms 210
## Q22_7_Take.an.antiviral.medicine 1103
## Q22_8_Take.no.action.to.treat.the.illness 1199
## Q22_9_Other 448
## r
## q Sometimes
## Q22_1_Go.to.a.doctor_s.office.or.medical.clinic 1235
## Q22_2_Decide.on.treatment.without.consulting.a.health.practitioner 1329
## Q22_3_Search.the.internet.for.a.treatment 861
## Q22_4_Get.adequate.sleep 875
## Q22_5_Eat.nutritious.food 1091
## Q22_6_Take-over-counter.medication.for.symptoms 1130
## Q22_7_Take.an.antiviral.medicine 877
## Q22_8_Take.no.action.to.treat.the.illness 839
## Q22_9_Other 38q22 <- Q22 %>%
count(q, r)Q23. Which of the following actions do you take when you have influenza symptoms to avoid someone else from getting sick?
Q23 <- data2 %>%
select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 123:Q23_11_Other) %>%
gather("q", "r", 7:Q23_11_Other)
with(Q23, table(q, r))## r
## q Always Never
## Q23_1_Stand.away.from.people 1006 135
## Q23_10_Cover.my.nose.and.mouth.when.I.sneeze.or.cough 1717 81
## Q23_11_Other 54 421
## Q23_2_Avoid.public.places 897 196
## Q23_3_Avoid.public.transportation 1342 245
## Q23_4_Stay.at.home 869 163
## Q23_5_Wash.my.hands.with.soap.more.often 1559 92
## Q23_6_Use.hand.sanitizers 1014 299
## Q23_7_Clean.the.surfaces.in.my.home 1151 153
## Q23_8_Clean.the.surfaces.I.use.at.work 856 508
## Q23_9_Cover.my.nose.and.mouth.with.a.surgical.mask 267 1463
## r
## q Sometimes
## Q23_1_Stand.away.from.people 996
## Q23_10_Cover.my.nose.and.mouth.when.I.sneeze.or.cough 341
## Q23_11_Other 28
## Q23_2_Avoid.public.places 1044
## Q23_3_Avoid.public.transportation 550
## Q23_4_Stay.at.home 1106
## Q23_5_Wash.my.hands.with.soap.more.often 488
## Q23_6_Use.hand.sanitizers 825
## Q23_7_Clean.the.surfaces.in.my.home 832
## Q23_8_Clean.the.surfaces.I.use.at.work 772
## Q23_9_Cover.my.nose.and.mouth.with.a.surgical.mask 409q23 <- Q23 %>%
count(q, r)Q24. What sources of information do you recall hearing or seeing about influenza outbreaks?
Q24 <- data2 %>%
select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 137:Q24_7_Refused) %>%
gather("q", "r", 7:Q24_6_Other)
with(Q24, table(q, r))## r
## q No Yes
## Q24_1_Print.media.such.as.newspapers.and.magazines 1460 708
## Q24_2_Traditional.media.such.as.television.and.radio 811 1357
## Q24_3_Social.media.such.as.internet.and.blogs 1680 488
## Q24_4_Word.of.mouth 1213 955
## Q24_5_None 1764 404
## Q24_6_Other 2114 54q24 <- Q24 %>%
count(q, r)Q25. If you received information from the news, internet or other public media that there was an influenza outbreak in your community would you do any of the following?
Q25 <- data2 %>%
select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 145:Q25_11_Other) %>%
gather("q", "r", 7:Q25_11_Other)
with(Q25, table(q, r))## r
## q Always Never
## Q25_1_Stand.away.from.people 649 217
## Q25_10_Cover.my.nose.and.mouth.when.I.sneeze.or.cough 1643 90
## Q25_11_Other 32 393
## Q25_2_Avoid.public.places 648 270
## Q25_3_Avoid.public.transportation 1221 268
## Q25_4_Stay.at.home 484 429
## Q25_5_Wash.my.hands.with.soap.more.often 1477 99
## Q25_6_Use.hand.sanitizers 1077 257
## Q25_7_Clean.the.surfaces.in.my.home 1116 160
## Q25_8_Clean.the.surfaces.I.use.at.work 902 464
## Q25_9_Cover.my.nose.and.mouth.with.a.surgical.mask 343 1286
## r
## q Sometimes
## Q25_1_Stand.away.from.people 1268
## Q25_10_Cover.my.nose.and.mouth.when.I.sneeze.or.cough 399
## Q25_11_Other 21
## Q25_2_Avoid.public.places 1217
## Q25_3_Avoid.public.transportation 643
## Q25_4_Stay.at.home 1222
## Q25_5_Wash.my.hands.with.soap.more.often 554
## Q25_6_Use.hand.sanitizers 799
## Q25_7_Clean.the.surfaces.in.my.home 857
## Q25_8_Clean.the.surfaces.I.use.at.work 766
## Q25_9_Cover.my.nose.and.mouth.with.a.surgical.mask 505q25 <- Q25 %>%
count(q, r)Q26. Does your household have children?
with(data2, table(Q26))## Q26
## No Yes
## 1570 576ggplot(data2[!is.na(data2$Q26), ]) + geom_bar(mapping = aes(x = Q26, fill = Q26), position = position_dodge())
Q27. What actions do you take when a child in your household has influenza symptoms?
Q27 <- data2 %>%
select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 159:Q27_4_Other) %>%
gather("q", "r", 7:Q27_4_Other)
with(Q27, table(q, r))## r
## q Always Never
## Q27_1_Keep.the.child.away.from.the.others.in.the.residence 198 90
## Q27_2_Keep.the.child.out.of.school-daycare 377 46
## Q27_3_Stop.child_s.social.activities.like.play.dates 388 41
## Q27_4_Other 12 93
## r
## q Sometimes
## Q27_1_Keep.the.child.away.from.the.others.in.the.residence 285
## Q27_2_Keep.the.child.out.of.school-daycare 149
## Q27_3_Stop.child_s.social.activities.like.play.dates 144
## Q27_4_Other 12q27 <- Q27 %>%
count(q, r)Q28. Are you a single parent?
with(data2, table(Q28))## Q28
## No Yes
## 490 86ggplot(data2[!is.na(data2$Q28), ]) + geom_bar(mapping = aes(x = Q28, fill = Q28), position = position_dodge())
Q29. How do you care for a sick child?
Q29 <- data2 %>%
select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 166:Q29_6_Other) %>%
gather("q", "r", 7:Q29_6_Other)
with(Q29, table(q, r))## r
## q Always Never Sometimes
## Q29_1_A.parent.brings.the.child.to.work 7 438 41
## Q29_2_A.parent.stays.home 266 27 193
## Q29_3_Another.adult.stays.home 68 202 216
## Q29_4_Send.the.child.to.school.sick 1 414 70
## Q29_5_Take.the.child.to.a.relative.or.friends 8 292 186
## Q29_6_Other 4 76 6q29 <- Q29 %>%
count(q, r)Q30. How do you care for a sick child?
Q30 <- data2 %>%
select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 174:Q30_6_Other) %>%
gather("q", "r", 7:Q30_6_Other)
with(Q30, table(q, r))## r
## q Always Never Sometimes
## Q30_1_I.bring.the.child.to.work 4 77 5
## Q30_2_I.stay.home 34 10 42
## Q30_3_Another.adult.stays.home 9 25 52
## Q30_4_Send.the.child.to.school.sick 3 60 23
## Q30_5_Take.the.child.to.a.relative.or.friends 7 33 46
## Q30_6_Other 1 14 3q30 <- Q30 %>%
count(q, r)Q31. How many hours of screen time (time spent watching television, a computer, smartphone, iPad, etc.) do you spend each day on average when you are not sick? Enter 0 if none
with(data2, summary(Q31))## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.868 6.000 24.000 52# by gender
with(data2, by(Q31, PPGENDER, summary))## PPGENDER: Female
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.838 6.000 21.000 21
## --------------------------------------------------------
## PPGENDER: Male
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.898 6.000 24.000 31#
with(data2, by(Q31, PPETHM, summary))## PPETHM: White, Non-Hispanic
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.693 6.000 22.000 29
## --------------------------------------------------------
## PPETHM: Black or African American, Non-Hispanic
## NULL
## --------------------------------------------------------
## PPETHM: Other, Non-Hispanic
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.477 5.250 15.000 5
## --------------------------------------------------------
## PPETHM: Hispanic
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 5.049 7.000 24.000 9
## --------------------------------------------------------
## PPETHM: 2+ races, Non-Hispanic
## NULL#
with(data2, by(Q31, PPINCIMP, summary))## PPINCIMP: Less than $5,000
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 1.0 4.0 4.7 6.0 20.0 3
## --------------------------------------------------------
## PPINCIMP: $5,000 to $7,499
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.750 4.000 5.458 7.250 20.000 1
## --------------------------------------------------------
## PPINCIMP: $7,500 to $9,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 3.000 4.000 6.077 11.000 14.000 1
## --------------------------------------------------------
## PPINCIMP: $10,000 to $12,499
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.855 7.000 16.000 1
## --------------------------------------------------------
## PPINCIMP: $12,500 to $14,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 3.000 5.000 5.778 8.000 20.000 3
## --------------------------------------------------------
## PPINCIMP: $15,000 to $19,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 5.129 6.000 24.000 1
## --------------------------------------------------------
## PPINCIMP: $20,000 to $24,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 3.000 5.000 5.362 6.000 22.000 4
## --------------------------------------------------------
## PPINCIMP: $25,000 to $29,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 3.000 4.500 5.184 6.000 18.000 1
## --------------------------------------------------------
## PPINCIMP: $30,000 to $34,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.750 4.000 4.886 6.000 15.000 5
## --------------------------------------------------------
## PPINCIMP: $35,000 to $39,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.832 7.000 18.000 3
## --------------------------------------------------------
## PPINCIMP: $40,000 to $49,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 3.000 4.000 5.063 7.000 17.000 6
## --------------------------------------------------------
## PPINCIMP: $50,000 to $59,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.608 6.000 20.000 3
## --------------------------------------------------------
## PPINCIMP: $60,000 to $74,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.876 6.000 20.000 6
## --------------------------------------------------------
## PPINCIMP: $75,000 to $84,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.335 5.750 16.000 2
## --------------------------------------------------------
## PPINCIMP: $85,000 to $99,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 2.00 4.00 4.75 6.00 21.00 1
## --------------------------------------------------------
## PPINCIMP: $100,000 to $124,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.975 7.000 24.000 4
## --------------------------------------------------------
## PPINCIMP: $125,000 to $149,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 5.038 7.000 20.000 1
## --------------------------------------------------------
## PPINCIMP: $150,000 to $174,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 4.000 4.659 6.000 20.000 3
## --------------------------------------------------------
## PPINCIMP: $175,000 or more
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 3.000 4.366 6.000 15.000 3Q32. How many hours of screen time do you spend each day on average when you are sick? Enter 0 if none
with(data2, summary(Q32))## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 4.000 4.267 6.000 24.000 61# by gender
with(data2, by(Q32, PPGENDER, summary))## PPGENDER: Female
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 4.000 4.181 6.000 24.000 29
## --------------------------------------------------------
## PPGENDER: Male
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 4.000 4.355 6.000 24.000 32#
with(data2, by(Q32, PPETHM, summary))## PPETHM: White, Non-Hispanic
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 4.000 4.258 6.000 24.000 39
## --------------------------------------------------------
## PPETHM: Black or African American, Non-Hispanic
## NULL
## --------------------------------------------------------
## PPETHM: Other, Non-Hispanic
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 2.000 3.102 4.250 12.000 5
## --------------------------------------------------------
## PPETHM: Hispanic
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 1.00 3.00 4.17 6.00 20.00 8
## --------------------------------------------------------
## PPETHM: 2+ races, Non-Hispanic
## NULL#
with(data2, by(Q32, PPINCIMP, summary))## PPINCIMP: Less than $5,000
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 2.000 3.837 5.000 24.000 4
## --------------------------------------------------------
## PPINCIMP: $5,000 to $7,499
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 1.00 2.00 4.92 7.00 24.00
## --------------------------------------------------------
## PPINCIMP: $7,500 to $9,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 2.000 3.667 7.250 12.000 2
## --------------------------------------------------------
## PPINCIMP: $10,000 to $12,499
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 3.000 4.074 6.000 15.000 2
## --------------------------------------------------------
## PPINCIMP: $12,500 to $14,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 2.500 3.935 5.750 20.000 2
## --------------------------------------------------------
## PPINCIMP: $15,000 to $19,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 2.000 3.672 5.000 18.000 2
## --------------------------------------------------------
## PPINCIMP: $20,000 to $24,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 5.000 5.074 8.000 20.000 5
## --------------------------------------------------------
## PPINCIMP: $25,000 to $29,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 1.00 3.00 4.48 7.00 22.00 1
## --------------------------------------------------------
## PPINCIMP: $30,000 to $34,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.159 6.250 12.000 5
## --------------------------------------------------------
## PPINCIMP: $35,000 to $39,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 3.000 4.218 6.000 18.000 3
## --------------------------------------------------------
## PPINCIMP: $40,000 to $49,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 3.000 3.958 6.000 16.000 5
## --------------------------------------------------------
## PPINCIMP: $50,000 to $59,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 3.000 3.774 6.000 15.000 7
## --------------------------------------------------------
## PPINCIMP: $60,000 to $74,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.387 6.000 20.000 7
## --------------------------------------------------------
## PPINCIMP: $75,000 to $84,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 4.000 4.258 6.000 14.000 1
## --------------------------------------------------------
## PPINCIMP: $85,000 to $99,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.282 6.000 21.000 4
## --------------------------------------------------------
## PPINCIMP: $100,000 to $124,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.467 6.000 24.000 4
## --------------------------------------------------------
## PPINCIMP: $125,000 to $149,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 5.000 4.656 6.000 20.000 1
## --------------------------------------------------------
## PPINCIMP: $150,000 to $174,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.542 6.000 20.000 2
## --------------------------------------------------------
## PPINCIMP: $175,000 or more
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 1.00 3.50 4.09 6.00 20.00 4Q33. How many people, including yourself, reside in your household?
with(data2, summary(Q33))## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 2.00 2.00 2.58 3.00 14.00 28# by gender
with(data2, by(Q33, PPGENDER, summary))## PPGENDER: Female
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 2.000 2.567 3.000 9.000 8
## --------------------------------------------------------
## PPGENDER: Male
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 2.000 2.594 3.000 14.000 20#
with(data2, by(Q31, PPETHM, summary))## PPETHM: White, Non-Hispanic
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.693 6.000 22.000 29
## --------------------------------------------------------
## PPETHM: Black or African American, Non-Hispanic
## NULL
## --------------------------------------------------------
## PPETHM: Other, Non-Hispanic
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.477 5.250 15.000 5
## --------------------------------------------------------
## PPETHM: Hispanic
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 5.049 7.000 24.000 9
## --------------------------------------------------------
## PPETHM: 2+ races, Non-Hispanic
## NULL#
with(data2, by(Q31, PPINCIMP, summary))## PPINCIMP: Less than $5,000
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 1.0 4.0 4.7 6.0 20.0 3
## --------------------------------------------------------
## PPINCIMP: $5,000 to $7,499
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.750 4.000 5.458 7.250 20.000 1
## --------------------------------------------------------
## PPINCIMP: $7,500 to $9,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 3.000 4.000 6.077 11.000 14.000 1
## --------------------------------------------------------
## PPINCIMP: $10,000 to $12,499
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.855 7.000 16.000 1
## --------------------------------------------------------
## PPINCIMP: $12,500 to $14,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 3.000 5.000 5.778 8.000 20.000 3
## --------------------------------------------------------
## PPINCIMP: $15,000 to $19,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 5.129 6.000 24.000 1
## --------------------------------------------------------
## PPINCIMP: $20,000 to $24,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 3.000 5.000 5.362 6.000 22.000 4
## --------------------------------------------------------
## PPINCIMP: $25,000 to $29,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 3.000 4.500 5.184 6.000 18.000 1
## --------------------------------------------------------
## PPINCIMP: $30,000 to $34,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.750 4.000 4.886 6.000 15.000 5
## --------------------------------------------------------
## PPINCIMP: $35,000 to $39,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.832 7.000 18.000 3
## --------------------------------------------------------
## PPINCIMP: $40,000 to $49,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 3.000 4.000 5.063 7.000 17.000 6
## --------------------------------------------------------
## PPINCIMP: $50,000 to $59,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.608 6.000 20.000 3
## --------------------------------------------------------
## PPINCIMP: $60,000 to $74,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.876 6.000 20.000 6
## --------------------------------------------------------
## PPINCIMP: $75,000 to $84,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.335 5.750 16.000 2
## --------------------------------------------------------
## PPINCIMP: $85,000 to $99,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 2.00 4.00 4.75 6.00 21.00 1
## --------------------------------------------------------
## PPINCIMP: $100,000 to $124,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 4.975 7.000 24.000 4
## --------------------------------------------------------
## PPINCIMP: $125,000 to $149,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.000 5.038 7.000 20.000 1
## --------------------------------------------------------
## PPINCIMP: $150,000 to $174,999
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 4.000 4.659 6.000 20.000 3
## --------------------------------------------------------
## PPINCIMP: $175,000 or more
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 3.000 4.366 6.000 15.000 3Household Members
HHM1
Q35. What is the gender of this member of the household? Remember, this relates to HHM1_Name who is HHM1_AGE years old.
with(data2, table(Q35))## Q35
## Female Male
## 799 859Q36. On average, how many days per week does this member of your household work or attend day care or school outside of your home?
with(data2, summary(Q36))## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 4.000 2.874 5.000 7.000 571Q38. On average, how many days per week does this member of your household use public transportation?
with(data2, summary(Q38))## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.3909 0.0000 7.0000 582Q39. How frequently does this member of your household visit a doctor’s office for wellness appointments?
with(data2, table(Q39))## Q39
## More than once per year Once per year Less than once per year
## 593 656 222
## Never Don_t know
## 100 84Q40. How frequently does this member of the household get sick in a typical year?
with(data2, table(Q40))## Q40
## Never 1 to 2 times 3 to 5 times 6 to 10 times More than 10
## 226 1025 271 32 13
## Don_t know
## 87Q41. How many times has this member of your household had influenza or another respiratory illness in the last two years?
with(data2, table(Q41))## Q41
## Never Once 2 times 3 times More than 3 Don_t know
## 807 400 191 60 39 158Q42. Does this member of your household get an annual influenza vaccine?
with(data2, table(Q42))## Q42
## Yes, always Yes, sometimes No, never Don_t know
## 661 263 567 166HHM2
Q43. What is the gender of this member of the household? Remember, this relates to HHM1_Name who is HHM1_AGE years old.
with(data2, table(Q43))## Q43
## Female Male
## 388 431Q44. On average, how many days per week does this member of your household work or attend day care or school outside of your home?
with(data2, summary(Q44))## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 5.000 3.669 5.000 7.000 1383Q46. On average, how many days per week does this member of your household use public transportation?
with(data2, summary(Q46))## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.5727 0.0000 7.0000 1391Q47. How frequently does this member of your household visit a doctor’s office for wellness appointments?
with(data2, table(Q47))## Q47
## More than once per year Once per year Less than once per year
## 232 353 125
## Never Don_t know
## 59 55Q48. How frequently does this member of the household get sick in a typical year?
with(data2, table(Q48))## Q48
## Never 1 to 2 times 3 to 5 times 6 to 10 times More than 10
## 83 490 153 25 7
## Don_t know
## 66Q49. How many times has this member of your household had influenza or another respiratory illness in the last two years?
with(data2, table(Q49))## Q49
## Never Once 2 times 3 times More than 3 Don_t know
## 403 183 91 32 21 93Q50. Does this member of your household get an annual influenza vaccine?
with(data2, table(Q50))## Q50
## Yes, always Yes, sometimes No, never Don_t know
## 275 132 317 100